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2023-08-28T00:00:00 | 2308.13137 | OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models | [
"Wenqi Shao",
"Mengzhao Chen",
"Zhaoyang Zhang",
"Peng Xu",
"Lirui Zhao",
"Zhiqian Li",
"Kaipeng Zhang",
"Peng Gao",
"Yu Qiao",
"Ping Luo"
] | https://github.com/OpenGVLab/OmniQuant | Large language models (LLMs) have revolutionized natural language processing tasks. However, their practical deployment is hindered by their immense memory and computation requirements. Although recent post-training quantization (PTQ) methods are effective in reducing memory footprint and improving the computational efficiency of LLM, they hand-craft quantization parameters, which leads to low performance and fails to deal with extremely low-bit quantization. To tackle this issue, we introduce an Omnidirectionally calibrated Quantization (OmniQuant) technique for LLMs, which achieves good performance in diverse quantization settings while maintaining the computational efficiency of PTQ by efficiently optimizing various quantization parameters. OmniQuant comprises two innovative components including Learnable Weight Clipping (LWC) and Learnable Equivalent Transformation (LET). LWC modulates the extreme values of weights by optimizing the clipping threshold. Meanwhile, LET tackles activation outliers by shifting the challenge of quantization from activations to weights through a learnable equivalent transformation. Operating within a differentiable framework using block-wise error minimization, OmniQuant can optimize the quantization process efficiently for both weight-only and weight-activation quantization. For instance, the LLaMA-2 model family with the size of 7-70B can be processed with OmniQuant on a single A100-40G GPU within 1-16 hours using 128 samples. Extensive experiments validate OmniQuant's superior performance across diverse quantization configurations such as W4A4, W6A6, W4A16, W3A16, and W2A16. Additionally, OmniQuant demonstrates effectiveness in instruction-tuned models and delivers notable improvements in inference speed and memory reduction on real devices. Codes and models are available at https://github.com/OpenGVLab/OmniQuant. |
2023-08-28T00:00:00 | 2308.13416 | SoTaNa: The Open-Source Software Development Assistant | [
"Ensheng Shi",
"Fengji Zhang",
"Yanlin Wang",
"Bei Chen",
"Lun Du",
"Hongyu Zhang",
"Shi Han",
"Dongmei Zhang",
"Hongbin Sun"
] | https://github.com/DeepSoftwareAnalytics/SoTaNa | Software development plays a crucial role in driving innovation and efficiency across modern societies. To meet the demands of this dynamic field, there is a growing need for an effective software development assistant. However, existing large language models represented by ChatGPT suffer from limited accessibility, including training data and model weights. Although other large open-source models like LLaMA have shown promise, they still struggle with understanding human intent. In this paper, we present SoTaNa, an open-source software development assistant. SoTaNa utilizes ChatGPT to generate high-quality instruction-based data for the domain of software engineering and employs a parameter-efficient fine-tuning approach to enhance the open-source foundation model, LLaMA. We evaluate the effectiveness of in answering Stack Overflow questions and demonstrate its capabilities. Additionally, we discuss its capabilities in code summarization and generation, as well as the impact of varying the volume of generated data on model performance. Notably, SoTaNa can run on a single GPU, making it accessible to a broader range of researchers. Our code, model weights, and data are public at https://github.com/DeepSoftwareAnalytics/SoTaNa. |
2023-08-28T00:00:00 | 2308.13404 | Relighting Neural Radiance Fields with Shadow and Highlight Hints | [
"Chong Zeng",
"Guojun Chen",
"Yue Dong",
"Pieter Peers",
"Hongzhi Wu",
"Xin Tong"
] | This paper presents a novel neural implicit radiance representation for free viewpoint relighting from a small set of unstructured photographs of an object lit by a moving point light source different from the view position. We express the shape as a signed distance function modeled by a multi layer perceptron. In contrast to prior relightable implicit neural representations, we do not disentangle the different reflectance components, but model both the local and global reflectance at each point by a second multi layer perceptron that, in addition, to density features, the current position, the normal (from the signed distace function), view direction, and light position, also takes shadow and highlight hints to aid the network in modeling the corresponding high frequency light transport effects. These hints are provided as a suggestion, and we leave it up to the network to decide how to incorporate these in the final relit result. We demonstrate and validate our neural implicit representation on synthetic and real scenes exhibiting a wide variety of shapes, material properties, and global illumination light transport. |
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2023-08-28T00:00:00 | 2308.13494 | Eventful Transformers: Leveraging Temporal Redundancy in Vision Transformers | [
"Matthew Dutson",
"Yin Li",
"Mohit Gupta"
] | Vision Transformers achieve impressive accuracy across a range of visual recognition tasks. Unfortunately, their accuracy frequently comes with high computational costs. This is a particular issue in video recognition, where models are often applied repeatedly across frames or temporal chunks. In this work, we exploit temporal redundancy between subsequent inputs to reduce the cost of Transformers for video processing. We describe a method for identifying and re-processing only those tokens that have changed significantly over time. Our proposed family of models, Eventful Transformers, can be converted from existing Transformers (often without any re-training) and give adaptive control over the compute cost at runtime. We evaluate our method on large-scale datasets for video object detection (ImageNet VID) and action recognition (EPIC-Kitchens 100). Our approach leads to significant computational savings (on the order of 2-4x) with only minor reductions in accuracy. |
|
2023-08-28T00:00:00 | 2308.13418 | Nougat: Neural Optical Understanding for Academic Documents | [
"Lukas Blecher",
"Guillem Cucurull",
"Thomas Scialom",
"Robert Stojnic"
] | https://github.com/facebookresearch/nougat | Scientific knowledge is predominantly stored in books and scientific journals, often in the form of PDFs. However, the PDF format leads to a loss of semantic information, particularly for mathematical expressions. We propose Nougat (Neural Optical Understanding for Academic Documents), a Visual Transformer model that performs an Optical Character Recognition (OCR) task for processing scientific documents into a markup language, and demonstrate the effectiveness of our model on a new dataset of scientific documents. The proposed approach offers a promising solution to enhance the accessibility of scientific knowledge in the digital age, by bridging the gap between human-readable documents and machine-readable text. We release the models and code to accelerate future work on scientific text recognition. |
2023-08-29T00:00:00 | 2308.14089 | MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records | [
"Scott L. Fleming",
"Alejandro Lozano",
"William J. Haberkorn",
"Jenelle A. Jindal",
"Eduardo P. Reis",
"Rahul Thapa",
"Louis Blankemeier",
"Julian Z. Genkins",
"Ethan Steinberg",
"Ashwin Nayak",
"Birju S. Patel",
"Chia-Chun Chiang",
"Alison Callahan",
"Zepeng Huo",
"Sergios Gatidis",
"Scott J. Adams",
"Oluseyi Fayanju",
"Shreya J. Shah",
"Thomas Savage",
"Ethan Goh",
"Akshay S. Chaudhari",
"Nima Aghaeepour",
"Christopher Sharp",
"Michael A. Pfeffer",
"Percy Liang",
"Jonathan H. Chen",
"Keith E. Morse",
"Emma P. Brunskill",
"Jason A. Fries",
"Nigam H. Shah"
] | The ability of large language models (LLMs) to follow natural language instructions with human-level fluency suggests many opportunities in healthcare to reduce administrative burden and improve quality of care. However, evaluating LLMs on realistic text generation tasks for healthcare remains challenging. Existing question answering datasets for electronic health record (EHR) data fail to capture the complexity of information needs and documentation burdens experienced by clinicians. To address these challenges, we introduce MedAlign, a benchmark dataset of 983 natural language instructions for EHR data. MedAlign is curated by 15 clinicians (7 specialities), includes clinician-written reference responses for 303 instructions, and provides 276 longitudinal EHRs for grounding instruction-response pairs. We used MedAlign to evaluate 6 general domain LLMs, having clinicians rank the accuracy and quality of each LLM response. We found high error rates, ranging from 35% (GPT-4) to 68% (MPT-7B-Instruct), and an 8.3% drop in accuracy moving from 32k to 2k context lengths for GPT-4. Finally, we report correlations between clinician rankings and automated natural language generation metrics as a way to rank LLMs without human review. We make MedAlign available under a research data use agreement to enable LLM evaluations on tasks aligned with clinician needs and preferences. |
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2023-08-29T00:00:00 | 2308.13785 | ORES: Open-vocabulary Responsible Visual Synthesis | [
"Minheng Ni",
"Chenfei Wu",
"Xiaodong Wang",
"Shengming Yin",
"Lijuan Wang",
"Zicheng Liu",
"Nan Duan"
] | Avoiding synthesizing specific visual concepts is an essential challenge in responsible visual synthesis. However, the visual concept that needs to be avoided for responsible visual synthesis tends to be diverse, depending on the region, context, and usage scenarios. In this work, we formalize a new task, Open-vocabulary Responsible Visual Synthesis (ORES), where the synthesis model is able to avoid forbidden visual concepts while allowing users to input any desired content. To address this problem, we present a Two-stage Intervention (TIN) framework. By introducing 1) rewriting with learnable instruction through a large-scale language model (LLM) and 2) synthesizing with prompt intervention on a diffusion synthesis model, it can effectively synthesize images avoiding any concepts but following the user's query as much as possible. To evaluate on ORES, we provide a publicly available dataset, baseline models, and benchmark. Experimental results demonstrate the effectiveness of our method in reducing risks of image generation. Our work highlights the potential of LLMs in responsible visual synthesis. Our code and dataset is public available. |
|
2023-08-31T00:00:00 | 2308.16149 | Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models | [
"Neha Sengupta",
"Sunil Kumar Sahu",
"Bokang Jia",
"Satheesh Katipomu",
"Haonan Li",
"Fajri Koto",
"Osama Mohammed Afzal",
"Samta Kamboj",
"Onkar Pandit",
"Rahul Pal",
"Lalit Pradhan",
"Zain Muhammad Mujahid",
"Massa Baali",
"Alham Fikri Aji",
"Zhengzhong Liu",
"Andy Hock",
"Andrew Feldman",
"Jonathan Lee",
"Andrew Jackson",
"Preslav Nakov",
"Timothy Baldwin",
"Eric Xing"
] | We introduce Jais and Jais-chat, new state-of-the-art Arabic-centric foundation and instruction-tuned open generative large language models (LLMs). The models are based on the GPT-3 decoder-only architecture and are pretrained on a mixture of Arabic and English texts, including source code in various programming languages. With 13 billion parameters, they demonstrate better knowledge and reasoning capabilities in Arabic than any existing open Arabic and multilingual models by a sizable margin, based on extensive evaluation. Moreover, the models are competitive in English compared to English-centric open models of similar size, despite being trained on much less English data. We provide a detailed description of the training, the tuning, the safety alignment, and the evaluation of the models. We release two open versions of the model -- the foundation Jais model, and an instruction-tuned Jais-chat variant -- with the aim of promoting research on Arabic LLMs. Available at https://huggingface.co/inception-mbzuai/jais-13b-chat |
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2023-08-31T00:00:00 | 2308.16137 | LM-Infinite: Simple On-the-Fly Length Generalization for Large Language Models | [
"Chi Han",
"Qifan Wang",
"Wenhan Xiong",
"Yu Chen",
"Heng Ji",
"Sinong Wang"
] | In recent years, there have been remarkable advancements in the performance of Transformer-based Large Language Models (LLMs) across various domains. As these LLMs are deployed for increasingly complex tasks, they often face the needs to conduct longer reasoning processes or understanding larger contexts. In these situations, the length generalization failure of LLMs on long sequences become more prominent. Most pre-training schemes truncate training sequences to a fixed length (such as 2048 for LLaMa). LLMs often struggle to generate fluent texts, let alone carry out downstream tasks, after longer contexts, even with relative positional encoding which is designed to cope with this problem. Common solutions such as finetuning on longer corpora often involves daunting hardware and time costs and requires careful training process design. To more efficiently leverage the generation capacity of existing LLMs, we theoretically and empirically investigate the main out-of-distribution (OOD) factors contributing to this problem. Inspired by this diagnosis, we propose a simple yet effective solution for on-the-fly length generalization, LM-Infinite, which involves only a Lambda-shaped attention mask and a distance limit while requiring no parameter updates or learning. We find it applicable to a variety of LLMs using relative-position encoding methods. LM-Infinite is computational efficient with O(n) time and space, and demonstrates consistent fluency and generation quality to as long as 32k tokens on ArXiv and OpenWebText2 datasets, with 2.72x decoding speedup. On downstream task such as passkey retrieval, it continues to work on inputs much longer than training lengths where vanilla models fail immediately. |
|
2023-08-31T00:00:00 | 2308.15930 | LLaSM: Large Language and Speech Model | [
"Yu Shu",
"Siwei Dong",
"Guangyao Chen",
"Wenhao Huang",
"Ruihua Zhang",
"Daochen Shi",
"Qiqi Xiang",
"Yemin Shi"
] | https://github.com/LinkSoul-AI/LLaSM | Multi-modal large language models have garnered significant interest recently. Though, most of the works focus on vision-language multi-modal models providing strong capabilities in following vision-and-language instructions. However, we claim that speech is also an important modality through which humans interact with the world. Hence, it is crucial for a general-purpose assistant to be able to follow multi-modal speech-and-language instructions. In this work, we propose Large Language and Speech Model (LLaSM). LLaSM is an end-to-end trained large multi-modal speech-language model with cross-modal conversational abilities, capable of following speech-and-language instructions. Our early experiments show that LLaSM demonstrates a more convenient and natural way for humans to interact with artificial intelligence. Specifically, we also release a large Speech Instruction Following dataset LLaSM-Audio-Instructions. Code and demo are available at https://github.com/LinkSoul-AI/LLaSM and https://huggingface.co/spaces/LinkSoul/LLaSM. The LLaSM-Audio-Instructions dataset is available at https://huggingface.co/datasets/LinkSoul/LLaSM-Audio-Instructions. |
2023-08-31T00:00:00 | 2308.15975 | RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation | [
"Mel Vecerik",
"Carl Doersch",
"Yi Yang",
"Todor Davchev",
"Yusuf Aytar",
"Guangyao Zhou",
"Raia Hadsell",
"Lourdes Agapito",
"Jon Scholz"
] | For robots to be useful outside labs and specialized factories we need a way to teach them new useful behaviors quickly. Current approaches lack either the generality to onboard new tasks without task-specific engineering, or else lack the data-efficiency to do so in an amount of time that enables practical use. In this work we explore dense tracking as a representational vehicle to allow faster and more general learning from demonstration. Our approach utilizes Track-Any-Point (TAP) models to isolate the relevant motion in a demonstration, and parameterize a low-level controller to reproduce this motion across changes in the scene configuration. We show this results in robust robot policies that can solve complex object-arrangement tasks such as shape-matching, stacking, and even full path-following tasks such as applying glue and sticking objects together, all from demonstrations that can be collected in minutes. |
|
2023-08-31T00:00:00 | 2308.15560 | WeatherBench 2: A benchmark for the next generation of data-driven global weather models | [
"Stephan Rasp",
"Stephan Hoyer",
"Alexander Merose",
"Ian Langmore",
"Peter Battaglia",
"Tyler Russel",
"Alvaro Sanchez-Gonzalez",
"Vivian Yang",
"Rob Carver",
"Shreya Agrawal",
"Matthew Chantry",
"Zied Ben Bouallegue",
"Peter Dueben",
"Carla Bromberg",
"Jared Sisk",
"Luke Barrington",
"Aaron Bell",
"Fei Sha"
] | WeatherBench 2 is an update to the global, medium-range (1-14 day) weather forecasting benchmark proposed by Rasp et al. (2020), designed with the aim to accelerate progress in data-driven weather modeling. WeatherBench 2 consists of an open-source evaluation framework, publicly available training, ground truth and baseline data as well as a continuously updated website with the latest metrics and state-of-the-art models: https://sites.research.google/weatherbench. This paper describes the design principles of the evaluation framework and presents results for current state-of-the-art physical and data-driven weather models. The metrics are based on established practices for evaluating weather forecasts at leading operational weather centers. We define a set of headline scores to provide an overview of model performance. In addition, we also discuss caveats in the current evaluation setup and challenges for the future of data-driven weather forecasting. |
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2023-08-31T00:00:00 | 2308.16185 | Learning Vision-based Pursuit-Evasion Robot Policies | [
"Andrea Bajcsy",
"Antonio Loquercio",
"Ashish Kumar",
"Jitendra Malik"
] | Learning strategic robot behavior -- like that required in pursuit-evasion interactions -- under real-world constraints is extremely challenging. It requires exploiting the dynamics of the interaction, and planning through both physical state and latent intent uncertainty. In this paper, we transform this intractable problem into a supervised learning problem, where a fully-observable robot policy generates supervision for a partially-observable one. We find that the quality of the supervision signal for the partially-observable pursuer policy depends on two key factors: the balance of diversity and optimality of the evader's behavior and the strength of the modeling assumptions in the fully-observable policy. We deploy our policy on a physical quadruped robot with an RGB-D camera on pursuit-evasion interactions in the wild. Despite all the challenges, the sensing constraints bring about creativity: the robot is pushed to gather information when uncertain, predict intent from noisy measurements, and anticipate in order to intercept. Project webpage: https://abajcsy.github.io/vision-based-pursuit/ |
|
2023-09-01T00:00:00 | 2308.16884 | The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants | [
"Lucas Bandarkar",
"Davis Liang",
"Benjamin Muller",
"Mikel Artetxe",
"Satya Narayan Shukla",
"Donald Husa",
"Naman Goyal",
"Abhinandan Krishnan",
"Luke Zettlemoyer",
"Madian Khabsa"
] | We present Belebele, a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. Significantly expanding the language coverage of natural language understanding (NLU) benchmarks, this dataset enables the evaluation of text models in high-, medium-, and low-resource languages. Each question is based on a short passage from the Flores-200 dataset and has four multiple-choice answers. The questions were carefully curated to discriminate between models with different levels of general language comprehension. The English dataset on its own proves difficult enough to challenge state-of-the-art language models. Being fully parallel, this dataset enables direct comparison of model performance across all languages. We use this dataset to evaluate the capabilities of multilingual masked language models (MLMs) and large language models (LLMs). We present extensive results and find that despite significant cross-lingual transfer in English-centric LLMs, much smaller MLMs pretrained on balanced multilingual data still understand far more languages. We also observe that larger vocabulary size and conscious vocabulary construction correlate with better performance on low-resource languages. Overall, Belebele opens up new avenues for evaluating and analyzing the multilingual capabilities of NLP systems. |
|
2023-09-01T00:00:00 | 2308.16824 | Can Programming Languages Boost Each Other via Instruction Tuning? | [
"Daoguang Zan",
"Ailun Yu",
"Bo Shen",
"Jiaxin Zhang",
"Taihong Chen",
"Bing Geng",
"Bei Chen",
"Jichuan Ji",
"Yafen Yao",
"Yongji Wang",
"Qianxiang Wang"
] | https://github.com/NL2Code/CodeM | When human programmers have mastered a programming language, it would be easier when they learn a new programming language. In this report, we focus on exploring whether programming languages can boost each other during the instruction fine-tuning phase of code large language models. We conduct extensive experiments of 8 popular programming languages (Python, JavaScript, TypeScript, C, C++, Java, Go, HTML) on StarCoder. Results demonstrate that programming languages can significantly improve each other. For example, CodeM-Python 15B trained on Python is able to increase Java by an absolute 17.95% pass@1 on HumanEval-X. More surprisingly, we found that CodeM-HTML 7B trained on the HTML corpus can improve Java by an absolute 15.24% pass@1. Our training data is released at https://github.com/NL2Code/CodeM. |
2023-09-01T00:00:00 | 2308.16458 | BioCoder: A Benchmark for Bioinformatics Code Generation with Contextual Pragmatic Knowledge | [
"Xiangru Tang",
"Bill Qian",
"Rick Gao",
"Jiakang Chen",
"Xinyun Chen",
"Mark Gerstein"
] | https://github.com/gersteinlab/biocoder | Pre-trained language models like ChatGPT have significantly improved code generation. As these models scale up, there is an increasing need for the output to handle more intricate tasks. Moreover, in bioinformatics, generating functional programs poses additional notable challenges due to the amount of domain knowledge, the need for complicated data operations, and intricate functional dependencies between the operations. Here, we present BioCoder, a benchmark developed to evaluate existing pre-trained models in generating bioinformatics code. In relation to function-code generation, BioCoder covers potential package dependencies, class declarations, and global variables. It incorporates 1026 functions and 1243 methods in Python and Java from GitHub and 253 examples from the Rosalind Project. BioCoder incorporates a fuzz-testing framework for evaluation, and we have applied it to evaluate many models including InCoder, CodeGen, CodeGen2, SantaCoder, StarCoder, StarCoder+, InstructCodeT5+, and ChatGPT. Our detailed analysis of these models emphasizes the importance of domain knowledge, pragmatic code generation, and contextual understanding. Our dataset, benchmark, Docker images, and scripts required for testing are all available at https://github.com/gersteinlab/biocoder. |
2023-09-01T00:00:00 | 2308.16512 | MVDream: Multi-view Diffusion for 3D Generation | [
"Yichun Shi",
"Peng Wang",
"Jianglong Ye",
"Mai Long",
"Kejie Li",
"Xiao Yang"
] | https://github.com/bytedance/MVDream | We propose MVDream, a multi-view diffusion model that is able to generate geometrically consistent multi-view images from a given text prompt. By leveraging image diffusion models pre-trained on large-scale web datasets and a multi-view dataset rendered from 3D assets, the resulting multi-view diffusion model can achieve both the generalizability of 2D diffusion and the consistency of 3D data. Such a model can thus be applied as a multi-view prior for 3D generation via Score Distillation Sampling, where it greatly improves the stability of existing 2D-lifting methods by solving the 3D consistency problem. Finally, we show that the multi-view diffusion model can also be fine-tuned under a few shot setting for personalized 3D generation, i.e. DreamBooth3D application, where the consistency can be maintained after learning the subject identity. |
2023-09-01T00:00:00 | 2308.16271 | Emergence of Segmentation with Minimalistic White-Box Transformers | [
"Yaodong Yu",
"Tianzhe Chu",
"Shengbang Tong",
"Ziyang Wu",
"Druv Pai",
"Sam Buchanan",
"Yi Ma"
] | https://github.com/Ma-Lab-Berkeley/CRATE | Transformer-like models for vision tasks have recently proven effective for a wide range of downstream applications such as segmentation and detection. Previous works have shown that segmentation properties emerge in vision transformers (ViTs) trained using self-supervised methods such as DINO, but not in those trained on supervised classification tasks. In this study, we probe whether segmentation emerges in transformer-based models solely as a result of intricate self-supervised learning mechanisms, or if the same emergence can be achieved under much broader conditions through proper design of the model architecture. Through extensive experimental results, we demonstrate that when employing a white-box transformer-like architecture known as CRATE, whose design explicitly models and pursues low-dimensional structures in the data distribution, segmentation properties, at both the whole and parts levels, already emerge with a minimalistic supervised training recipe. Layer-wise finer-grained analysis reveals that the emergent properties strongly corroborate the designed mathematical functions of the white-box network. Our results suggest a path to design white-box foundation models that are simultaneously highly performant and mathematically fully interpretable. Code is at https://github.com/Ma-Lab-Berkeley/CRATE. |
2023-09-01T00:00:00 | 2308.16246 | Active Neural Mapping | [
"Zike Yan",
"Haoxiang Yang",
"Hongbin Zha"
] | We address the problem of active mapping with a continually-learned neural scene representation, namely Active Neural Mapping. The key lies in actively finding the target space to be explored with efficient agent movement, thus minimizing the map uncertainty on-the-fly within a previously unseen environment. In this paper, we examine the weight space of the continually-learned neural field, and show empirically that the neural variability, the prediction robustness against random weight perturbation, can be directly utilized to measure the instant uncertainty of the neural map. Together with the continuous geometric information inherited in the neural map, the agent can be guided to find a traversable path to gradually gain knowledge of the environment. We present for the first time an active mapping system with a coordinate-based implicit neural representation for online scene reconstruction. Experiments in the visually-realistic Gibson and Matterport3D environment demonstrate the efficacy of the proposed method. |
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2023-09-01T00:00:00 | 2308.16876 | SportsSloMo: A New Benchmark and Baselines for Human-centric Video Frame Interpolation | [
"Jiaben Chen",
"Huaizu Jiang"
] | Human-centric video frame interpolation has great potential for improving people's entertainment experiences and finding commercial applications in the sports analysis industry, e.g., synthesizing slow-motion videos. Although there are multiple benchmark datasets available in the community, none of them is dedicated for human-centric scenarios. To bridge this gap, we introduce SportsSloMo, a benchmark consisting of more than 130K video clips and 1M video frames of high-resolution (geq720p) slow-motion sports videos crawled from YouTube. We re-train several state-of-the-art methods on our benchmark, and the results show a decrease in their accuracy compared to other datasets. It highlights the difficulty of our benchmark and suggests that it poses significant challenges even for the best-performing methods, as human bodies are highly deformable and occlusions are frequent in sports videos. To improve the accuracy, we introduce two loss terms considering the human-aware priors, where we add auxiliary supervision to panoptic segmentation and human keypoints detection, respectively. The loss terms are model agnostic and can be easily plugged into any video frame interpolation approaches. Experimental results validate the effectiveness of our proposed loss terms, leading to consistent performance improvement over 5 existing models, which establish strong baseline models on our benchmark. The dataset and code can be found at: https://neu-vi.github.io/SportsSlomo/. |
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2023-09-01T00:00:00 | 2308.16891 | GNFactor: Multi-Task Real Robot Learning with Generalizable Neural Feature Fields | [
"Yanjie Ze",
"Ge Yan",
"Yueh-Hua Wu",
"Annabella Macaluso",
"Yuying Ge",
"Jianglong Ye",
"Nicklas Hansen",
"Li Erran Li",
"Xiaolong Wang"
] | It is a long-standing problem in robotics to develop agents capable of executing diverse manipulation tasks from visual observations in unstructured real-world environments. To achieve this goal, the robot needs to have a comprehensive understanding of the 3D structure and semantics of the scene. In this work, we present GNFactor, a visual behavior cloning agent for multi-task robotic manipulation with Generalizable Neural feature Fields. GNFactor jointly optimizes a generalizable neural field (GNF) as a reconstruction module and a Perceiver Transformer as a decision-making module, leveraging a shared deep 3D voxel representation. To incorporate semantics in 3D, the reconstruction module utilizes a vision-language foundation model (e.g., Stable Diffusion) to distill rich semantic information into the deep 3D voxel. We evaluate GNFactor on 3 real robot tasks and perform detailed ablations on 10 RLBench tasks with a limited number of demonstrations. We observe a substantial improvement of GNFactor over current state-of-the-art methods in seen and unseen tasks, demonstrating the strong generalization ability of GNFactor. Our project website is https://yanjieze.com/GNFactor/ . |
|
2023-09-01T00:00:00 | 2308.16582 | Any-Size-Diffusion: Toward Efficient Text-Driven Synthesis for Any-Size HD Images | [
"Qingping Zheng",
"Yuanfan Guo",
"Jiankang Deng",
"Jianhua Han",
"Ying Li",
"Songcen Xu",
"Hang Xu"
] | Stable diffusion, a generative model used in text-to-image synthesis, frequently encounters resolution-induced composition problems when generating images of varying sizes. This issue primarily stems from the model being trained on pairs of single-scale images and their corresponding text descriptions. Moreover, direct training on images of unlimited sizes is unfeasible, as it would require an immense number of text-image pairs and entail substantial computational expenses. To overcome these challenges, we propose a two-stage pipeline named Any-Size-Diffusion (ASD), designed to efficiently generate well-composed images of any size, while minimizing the need for high-memory GPU resources. Specifically, the initial stage, dubbed Any Ratio Adaptability Diffusion (ARAD), leverages a selected set of images with a restricted range of ratios to optimize the text-conditional diffusion model, thereby improving its ability to adjust composition to accommodate diverse image sizes. To support the creation of images at any desired size, we further introduce a technique called Fast Seamless Tiled Diffusion (FSTD) at the subsequent stage. This method allows for the rapid enlargement of the ASD output to any high-resolution size, avoiding seaming artifacts or memory overloads. Experimental results on the LAION-COCO and MM-CelebA-HQ benchmarks demonstrate that ASD can produce well-structured images of arbitrary sizes, cutting down the inference time by 2x compared to the traditional tiled algorithm. |
|
2023-09-04T00:00:00 | 2309.00071 | YaRN: Efficient Context Window Extension of Large Language Models | [
"Bowen Peng",
"Jeffrey Quesnelle",
"Honglu Fan",
"Enrico Shippole"
] | https://github.com/jquesnelle/yarn | Rotary Position Embeddings (RoPE) have been shown to effectively encode positional information in transformer-based language models. However, these models fail to generalize past the sequence length they were trained on. We present YaRN (Yet another RoPE extensioN method), a compute-efficient method to extend the context window of such models, requiring 10x less tokens and 2.5x less training steps than previous methods. Using YaRN, we show that LLaMA models can effectively utilize and extrapolate to context lengths much longer than their original pre-training would allow, while also surpassing previous the state-of-the-art at context window extension. In addition, we demonstrate that YaRN exhibits the capability to extrapolate beyond the limited context of a fine-tuning dataset. We publish the checkpoints of Llama 2 7B/13B fine-tuned using YaRN with 64k and 128k context windows at https://github.com/jquesnelle/yarn |
2023-09-04T00:00:00 | 2309.00035 | FACET: Fairness in Computer Vision Evaluation Benchmark | [
"Laura Gustafson",
"Chloe Rolland",
"Nikhila Ravi",
"Quentin Duval",
"Aaron Adcock",
"Cheng-Yang Fu",
"Melissa Hall",
"Candace Ross"
] | Computer vision models have known performance disparities across attributes such as gender and skin tone. This means during tasks such as classification and detection, model performance differs for certain classes based on the demographics of the people in the image. These disparities have been shown to exist, but until now there has not been a unified approach to measure these differences for common use-cases of computer vision models. We present a new benchmark named FACET (FAirness in Computer Vision EvaluaTion), a large, publicly available evaluation set of 32k images for some of the most common vision tasks - image classification, object detection and segmentation. For every image in FACET, we hired expert reviewers to manually annotate person-related attributes such as perceived skin tone and hair type, manually draw bounding boxes and label fine-grained person-related classes such as disk jockey or guitarist. In addition, we use FACET to benchmark state-of-the-art vision models and present a deeper understanding of potential performance disparities and challenges across sensitive demographic attributes. With the exhaustive annotations collected, we probe models using single demographics attributes as well as multiple attributes using an intersectional approach (e.g. hair color and perceived skin tone). Our results show that classification, detection, segmentation, and visual grounding models exhibit performance disparities across demographic attributes and intersections of attributes. These harms suggest that not all people represented in datasets receive fair and equitable treatment in these vision tasks. We hope current and future results using our benchmark will contribute to fairer, more robust vision models. FACET is available publicly at https://facet.metademolab.com/ |
|
2023-09-04T00:00:00 | 2309.00359 | Large Content And Behavior Models To Understand, Simulate, And Optimize Content And Behavior | [
"Ashmit Khandelwal",
"Aditya Agrawal",
"Aanisha Bhattacharyya",
"Yaman K Singla",
"Somesh Singh",
"Uttaran Bhattacharya",
"Ishita Dasgupta",
"Stefano Petrangeli",
"Rajiv Ratn Shah",
"Changyou Chen",
"Balaji Krishnamurthy"
] | Shannon, in his seminal paper introducing information theory, divided the communication into three levels: technical, semantic, and effectivenss. While the technical level is concerned with accurate reconstruction of transmitted symbols, the semantic and effectiveness levels deal with the inferred meaning and its effect on the receiver. Thanks to telecommunications, the first level problem has produced great advances like the internet. Large Language Models (LLMs) make some progress towards the second goal, but the third level still remains largely untouched. The third problem deals with predicting and optimizing communication for desired receiver behavior. LLMs, while showing wide generalization capabilities across a wide range of tasks, are unable to solve for this. One reason for the underperformance could be a lack of "behavior tokens" in LLMs' training corpora. Behavior tokens define receiver behavior over a communication, such as shares, likes, clicks, purchases, retweets, etc. While preprocessing data for LLM training, behavior tokens are often removed from the corpora as noise. Therefore, in this paper, we make some initial progress towards reintroducing behavior tokens in LLM training. The trained models, other than showing similar performance to LLMs on content understanding tasks, show generalization capabilities on behavior simulation, content simulation, behavior understanding, and behavior domain adaptation. Using a wide range of tasks on two corpora, we show results on all these capabilities. We call these models Large Content and Behavior Models (LCBMs). Further, to spur more research on LCBMs, we release our new Content Behavior Corpus (CBC), a repository containing communicator, message, and corresponding receiver behavior. |
|
2023-09-04T00:00:00 | 2309.00267 | RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback | [
"Harrison Lee",
"Samrat Phatale",
"Hassan Mansoor",
"Kellie Lu",
"Thomas Mesnard",
"Colton Bishop",
"Victor Carbune",
"Abhinav Rastogi"
] | Reinforcement learning from human feedback (RLHF) is effective at aligning large language models (LLMs) to human preferences, but gathering high quality human preference labels is a key bottleneck. We conduct a head-to-head comparison of RLHF vs. RL from AI Feedback (RLAIF) - a technique where preferences are labeled by an off-the-shelf LLM in lieu of humans, and we find that they result in similar improvements. On the task of summarization, human evaluators prefer generations from both RLAIF and RLHF over a baseline supervised fine-tuned model in ~70% of cases. Furthermore, when asked to rate RLAIF vs. RLHF summaries, humans prefer both at equal rates. These results suggest that RLAIF can yield human-level performance, offering a potential solution to the scalability limitations of RLHF. |
|
2023-09-04T00:00:00 | 2309.00610 | CityDreamer: Compositional Generative Model of Unbounded 3D Cities | [
"Haozhe Xie",
"Zhaoxi Chen",
"Fangzhou Hong",
"Ziwei Liu"
] | In recent years, extensive research has focused on 3D natural scene generation, but the domain of 3D city generation has not received as much exploration. This is due to the greater challenges posed by 3D city generation, mainly because humans are more sensitive to structural distortions in urban environments. Additionally, generating 3D cities is more complex than 3D natural scenes since buildings, as objects of the same class, exhibit a wider range of appearances compared to the relatively consistent appearance of objects like trees in natural scenes. To address these challenges, we propose CityDreamer, a compositional generative model designed specifically for unbounded 3D cities, which separates the generation of building instances from other background objects, such as roads, green lands, and water areas, into distinct modules. Furthermore, we construct two datasets, OSM and GoogleEarth, containing a vast amount of real-world city imagery to enhance the realism of the generated 3D cities both in their layouts and appearances. Through extensive experiments, CityDreamer has proven its superiority over state-of-the-art methods in generating a wide range of lifelike 3D cities. |
|
2023-09-04T00:00:00 | 2309.00398 | VideoGen: A Reference-Guided Latent Diffusion Approach for High Definition Text-to-Video Generation | [
"Xin Li",
"Wenqing Chu",
"Ye Wu",
"Weihang Yuan",
"Fanglong Liu",
"Qi Zhang",
"Fu Li",
"Haocheng Feng",
"Errui Ding",
"Jingdong Wang"
] | In this paper, we present VideoGen, a text-to-video generation approach, which can generate a high-definition video with high frame fidelity and strong temporal consistency using reference-guided latent diffusion. We leverage an off-the-shelf text-to-image generation model, e.g., Stable Diffusion, to generate an image with high content quality from the text prompt, as a reference image to guide video generation. Then, we introduce an efficient cascaded latent diffusion module conditioned on both the reference image and the text prompt, for generating latent video representations, followed by a flow-based temporal upsampling step to improve the temporal resolution. Finally, we map latent video representations into a high-definition video through an enhanced video decoder. During training, we use the first frame of a ground-truth video as the reference image for training the cascaded latent diffusion module. The main characterises of our approach include: the reference image generated by the text-to-image model improves the visual fidelity; using it as the condition makes the diffusion model focus more on learning the video dynamics; and the video decoder is trained over unlabeled video data, thus benefiting from high-quality easily-available videos. VideoGen sets a new state-of-the-art in text-to-video generation in terms of both qualitative and quantitative evaluation. |
|
2023-09-04T00:00:00 | 2309.00615 | Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following | [
"Ziyu Guo",
"Renrui Zhang",
"Xiangyang Zhu",
"Yiwen Tang",
"Xianzheng Ma",
"Jiaming Han",
"Kexin Chen",
"Peng Gao",
"Xianzhi Li",
"Hongsheng Li",
"Pheng-Ann Heng"
] | https://github.com/ZiyuGuo99/Point-Bind_Point-LLM | We introduce Point-Bind, a 3D multi-modality model aligning point clouds with 2D image, language, audio, and video. Guided by ImageBind, we construct a joint embedding space between 3D and multi-modalities, enabling many promising applications, e.g., any-to-3D generation, 3D embedding arithmetic, and 3D open-world understanding. On top of this, we further present Point-LLM, the first 3D large language model (LLM) following 3D multi-modal instructions. By parameter-efficient fine-tuning techniques, Point-LLM injects the semantics of Point-Bind into pre-trained LLMs, e.g., LLaMA, which requires no 3D instruction data, but exhibits superior 3D and multi-modal question-answering capacity. We hope our work may cast a light on the community for extending 3D point clouds to multi-modality applications. Code is available at https://github.com/ZiyuGuo99/Point-Bind_Point-LLM. |
2023-09-06T00:00:00 | 2309.02285 | PromptTTS 2: Describing and Generating Voices with Text Prompt | [
"Yichong Leng",
"Zhifang Guo",
"Kai Shen",
"Xu Tan",
"Zeqian Ju",
"Yanqing Liu",
"Yufei Liu",
"Dongchao Yang",
"Leying Zhang",
"Kaitao Song",
"Lei He",
"Xiang-Yang Li",
"Sheng Zhao",
"Tao Qin",
"Jiang Bian"
] | Speech conveys more information than just text, as the same word can be uttered in various voices to convey diverse information. Compared to traditional text-to-speech (TTS) methods relying on speech prompts (reference speech) for voice variability, using text prompts (descriptions) is more user-friendly since speech prompts can be hard to find or may not exist at all. TTS approaches based on the text prompt face two challenges: 1) the one-to-many problem, where not all details about voice variability can be described in the text prompt, and 2) the limited availability of text prompt datasets, where vendors and large cost of data labeling are required to write text prompt for speech. In this work, we introduce PromptTTS 2 to address these challenges with a variation network to provide variability information of voice not captured by text prompts, and a prompt generation pipeline to utilize the large language models (LLM) to compose high quality text prompts. Specifically, the variation network predicts the representation extracted from the reference speech (which contains full information about voice) based on the text prompt representation. For the prompt generation pipeline, it generates text prompts for speech with a speech understanding model to recognize voice attributes (e.g., gender, speed) from speech and a large language model to formulate text prompt based on the recognition results. Experiments on a large-scale (44K hours) speech dataset demonstrate that compared to the previous works, PromptTTS 2 generates voices more consistent with text prompts and supports the sampling of diverse voice variability, thereby offering users more choices on voice generation. Additionally, the prompt generation pipeline produces high-quality prompts, eliminating the large labeling cost. The demo page of PromptTTS 2 is available onlinehttps://speechresearch.github.io/prompttts2. |
|
2023-09-06T00:00:00 | 2309.00754 | Efficient RLHF: Reducing the Memory Usage of PPO | [
"Michael Santacroce",
"Yadong Lu",
"Han Yu",
"Yuanzhi Li",
"Yelong Shen"
] | Reinforcement Learning with Human Feedback (RLHF) has revolutionized language modeling by aligning models with human preferences. However, the RL stage, Proximal Policy Optimization (PPO), requires over 3x the memory of Supervised Fine-Tuning (SFT), making it infeasible to use for most practitioners. To address this issue, we present a comprehensive analysis the memory usage, performance, and training time of memory-savings techniques for PPO. We introduce Hydra-RLHF by first integrating the SFT and Reward models and then dynamically turning LoRA "off" during training. Our experiments show: 1. Using LoRA during PPO reduces its memory usage to be smaller than SFT while improving alignment across four public benchmarks, and 2. Hydra-PPO reduces the latency per sample of LoRA-PPO by up to 65% while maintaining its performance. Our results demonstrate that Hydra-PPO is a simple and promising solution for enabling more widespread usage of RLHF. |
|
2023-09-06T00:00:00 | 2309.00986 | ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models | [
"Chenliang Li",
"Hehong Chen",
"Ming Yan",
"Weizhou Shen",
"Haiyang Xu",
"Zhikai Wu",
"Zhicheng Zhang",
"Wenmeng Zhou",
"Yingda Chen",
"Chen Cheng",
"Hongzhu Shi",
"Ji Zhang",
"Fei Huang",
"Jingren Zhou"
] | https://github.com/modelscope/modelscope-agent | Large language models (LLMs) have recently demonstrated remarkable capabilities to comprehend human intentions, engage in reasoning, and design planning-like behavior. To further unleash the power of LLMs to accomplish complex tasks, there is a growing trend to build agent framework that equips LLMs, such as ChatGPT, with tool-use abilities to connect with massive external APIs. In this work, we introduce ModelScope-Agent, a general and customizable agent framework for real-world applications, based on open-source LLMs as controllers. It provides a user-friendly system library, with customizable engine design to support model training on multiple open-source LLMs, while also enabling seamless integration with both model APIs and common APIs in a unified way. To equip the LLMs with tool-use abilities, a comprehensive framework has been proposed spanning over tool-use data collection, tool retrieval, tool registration, memory control, customized model training, and evaluation for practical real-world applications. Finally, we showcase ModelScopeGPT, a real-world intelligent assistant of ModelScope Community based on the ModelScope-Agent framework, which is able to connect open-source LLMs with more than 1000 public AI models and localized community knowledge in ModelScope. The ModelScope-Agent libraryhttps://github.com/modelscope/modelscope-agent and online demohttps://modelscope.cn/studios/damo/ModelScopeGPT/summary are now publicly available. |
2023-09-06T00:00:00 | 2309.01770 | StyleAdapter: A Single-Pass LoRA-Free Model for Stylized Image Generation | [
"Zhouxia Wang",
"Xintao Wang",
"Liangbin Xie",
"Zhongang Qi",
"Ying Shan",
"Wenping Wang",
"Ping Luo"
] | This paper presents a LoRA-free method for stylized image generation that takes a text prompt and style reference images as inputs and produces an output image in a single pass. Unlike existing methods that rely on training a separate LoRA for each style, our method can adapt to various styles with a unified model. However, this poses two challenges: 1) the prompt loses controllability over the generated content, and 2) the output image inherits both the semantic and style features of the style reference image, compromising its content fidelity. To address these challenges, we introduce StyleAdapter, a model that comprises two components: a two-path cross-attention module (TPCA) and three decoupling strategies. These components enable our model to process the prompt and style reference features separately and reduce the strong coupling between the semantic and style information in the style references. StyleAdapter can generate high-quality images that match the content of the prompts and adopt the style of the references (even for unseen styles) in a single pass, which is more flexible and efficient than previous methods. Experiments have been conducted to demonstrate the superiority of our method over previous works. |
|
2023-09-06T00:00:00 | 2309.01700 | ControlMat: A Controlled Generative Approach to Material Capture | [
"Giuseppe Vecchio",
"Rosalie Martin",
"Arthur Roullier",
"Adrien Kaiser",
"Romain Rouffet",
"Valentin Deschaintre",
"Tamy Boubekeur"
] | Material reconstruction from a photograph is a key component of 3D content creation democratization. We propose to formulate this ill-posed problem as a controlled synthesis one, leveraging the recent progress in generative deep networks. We present ControlMat, a method which, given a single photograph with uncontrolled illumination as input, conditions a diffusion model to generate plausible, tileable, high-resolution physically-based digital materials. We carefully analyze the behavior of diffusion models for multi-channel outputs, adapt the sampling process to fuse multi-scale information and introduce rolled diffusion to enable both tileability and patched diffusion for high-resolution outputs. Our generative approach further permits exploration of a variety of materials which could correspond to the input image, mitigating the unknown lighting conditions. We show that our approach outperforms recent inference and latent-space-optimization methods, and carefully validate our diffusion process design choices. Supplemental materials and additional details are available at: https://gvecchio.com/controlmat/. |
|
2023-09-06T00:00:00 | 2309.00908 | MagicProp: Diffusion-based Video Editing via Motion-aware Appearance Propagation | [
"Hanshu Yan",
"Jun Hao Liew",
"Long Mai",
"Shanchuan Lin",
"Jiashi Feng"
] | This paper addresses the issue of modifying the visual appearance of videos while preserving their motion. A novel framework, named MagicProp, is proposed, which disentangles the video editing process into two stages: appearance editing and motion-aware appearance propagation. In the first stage, MagicProp selects a single frame from the input video and applies image-editing techniques to modify the content and/or style of the frame. The flexibility of these techniques enables the editing of arbitrary regions within the frame. In the second stage, MagicProp employs the edited frame as an appearance reference and generates the remaining frames using an autoregressive rendering approach. To achieve this, a diffusion-based conditional generation model, called PropDPM, is developed, which synthesizes the target frame by conditioning on the reference appearance, the target motion, and its previous appearance. The autoregressive editing approach ensures temporal consistency in the resulting videos. Overall, MagicProp combines the flexibility of image-editing techniques with the superior temporal consistency of autoregressive modeling, enabling flexible editing of object types and aesthetic styles in arbitrary regions of input videos while maintaining good temporal consistency across frames. Extensive experiments in various video editing scenarios demonstrate the effectiveness of MagicProp. |
|
2023-09-06T00:00:00 | 2309.00775 | Contrastive Feature Masking Open-Vocabulary Vision Transformer | [
"Dahun Kim",
"Anelia Angelova",
"Weicheng Kuo"
] | We present Contrastive Feature Masking Vision Transformer (CFM-ViT) - an image-text pretraining methodology that achieves simultaneous learning of image- and region-level representation for open-vocabulary object detection (OVD). Our approach combines the masked autoencoder (MAE) objective into the contrastive learning objective to improve the representation for localization tasks. Unlike standard MAE, we perform reconstruction in the joint image-text embedding space, rather than the pixel space as is customary with the classical MAE method, which causes the model to better learn region-level semantics. Moreover, we introduce Positional Embedding Dropout (PED) to address scale variation between image-text pretraining and detection finetuning by randomly dropping out the positional embeddings during pretraining. PED improves detection performance and enables the use of a frozen ViT backbone as a region classifier, preventing the forgetting of open-vocabulary knowledge during detection finetuning. On LVIS open-vocabulary detection benchmark, CFM-ViT achieves a state-of-the-art 33.9 APr, surpassing the best approach by 7.6 points and achieves better zero-shot detection transfer. Finally, CFM-ViT acquires strong image-level representation, outperforming the state of the art on 8 out of 12 metrics on zero-shot image-text retrieval benchmarks. |
|
2023-09-06T00:00:00 | 2309.02119 | Hierarchical Masked 3D Diffusion Model for Video Outpainting | [
"Fanda Fan",
"Chaoxu Guo",
"Litong Gong",
"Biao Wang",
"Tiezheng Ge",
"Yuning Jiang",
"Chunjie Luo",
"Jianfeng Zhan"
] | Video outpainting aims to adequately complete missing areas at the edges of video frames. Compared to image outpainting, it presents an additional challenge as the model should maintain the temporal consistency of the filled area. In this paper, we introduce a masked 3D diffusion model for video outpainting. We use the technique of mask modeling to train the 3D diffusion model. This allows us to use multiple guide frames to connect the results of multiple video clip inferences, thus ensuring temporal consistency and reducing jitter between adjacent frames. Meanwhile, we extract the global frames of the video as prompts and guide the model to obtain information other than the current video clip using cross-attention. We also introduce a hybrid coarse-to-fine inference pipeline to alleviate the artifact accumulation problem. The existing coarse-to-fine pipeline only uses the infilling strategy, which brings degradation because the time interval of the sparse frames is too large. Our pipeline benefits from bidirectional learning of the mask modeling and thus can employ a hybrid strategy of infilling and interpolation when generating sparse frames. Experiments show that our method achieves state-of-the-art results in video outpainting tasks. More results are provided at our https://fanfanda.github.io/M3DDM/. |
|
2023-09-06T00:00:00 | 2309.02040 | Diffusion Generative Inverse Design | [
"Marin Vlastelica",
"Tatiana López-Guevara",
"Kelsey Allen",
"Peter Battaglia",
"Arnaud Doucet",
"Kimberley Stachenfeld"
] | Inverse design refers to the problem of optimizing the input of an objective function in order to enact a target outcome. For many real-world engineering problems, the objective function takes the form of a simulator that predicts how the system state will evolve over time, and the design challenge is to optimize the initial conditions that lead to a target outcome. Recent developments in learned simulation have shown that graph neural networks (GNNs) can be used for accurate, efficient, differentiable estimation of simulator dynamics, and support high-quality design optimization with gradient- or sampling-based optimization procedures. However, optimizing designs from scratch requires many expensive model queries, and these procedures exhibit basic failures on either non-convex or high-dimensional problems.In this work, we show how denoising diffusion models (DDMs) can be used to solve inverse design problems efficiently and propose a particle sampling algorithm for further improving their efficiency. We perform experiments on a number of fluid dynamics design challenges, and find that our approach substantially reduces the number of calls to the simulator compared to standard techniques. |
|
2023-09-06T00:00:00 | 2309.00966 | Compositional Diffusion-Based Continuous Constraint Solvers | [
"Zhutian Yang",
"Jiayuan Mao",
"Yilun Du",
"Jiajun Wu",
"Joshua B. Tenenbaum",
"Tomás Lozano-Pérez",
"Leslie Pack Kaelbling"
] | This paper introduces an approach for learning to solve continuous constraint satisfaction problems (CCSP) in robotic reasoning and planning. Previous methods primarily rely on hand-engineering or learning generators for specific constraint types and then rejecting the value assignments when other constraints are violated. By contrast, our model, the compositional diffusion continuous constraint solver (Diffusion-CCSP) derives global solutions to CCSPs by representing them as factor graphs and combining the energies of diffusion models trained to sample for individual constraint types. Diffusion-CCSP exhibits strong generalization to novel combinations of known constraints, and it can be integrated into a task and motion planner to devise long-horizon plans that include actions with both discrete and continuous parameters. Project site: https://diffusion-ccsp.github.io/ |
|
2023-09-06T00:00:00 | 2309.01826 | One Wide Feedforward is All You Need | [
"Telmo Pessoa Pires",
"António V. Lopes",
"Yannick Assogba",
"Hendra Setiawan"
] | The Transformer architecture has two main non-embedding components: Attention and the Feed Forward Network (FFN). Attention captures interdependencies between words regardless of their position, while the FFN non-linearly transforms each input token independently. In this work we explore the role of the FFN, and find that despite taking up a significant fraction of the model's parameters, it is highly redundant. Concretely, we are able to substantially reduce the number of parameters with only a modest drop in accuracy by removing the FFN on the decoder layers and sharing a single FFN across the encoder. Finally we scale this architecture back to its original size by increasing the hidden dimension of the shared FFN, achieving substantial gains in both accuracy and latency with respect to the original Transformer Big. |
|
2023-09-06T00:00:00 | 2309.02420 | Doppelgangers: Learning to Disambiguate Images of Similar Structures | [
"Ruojin Cai",
"Joseph Tung",
"Qianqian Wang",
"Hadar Averbuch-Elor",
"Bharath Hariharan",
"Noah Snavely"
] | We consider the visual disambiguation task of determining whether a pair of visually similar images depict the same or distinct 3D surfaces (e.g., the same or opposite sides of a symmetric building). Illusory image matches, where two images observe distinct but visually similar 3D surfaces, can be challenging for humans to differentiate, and can also lead 3D reconstruction algorithms to produce erroneous results. We propose a learning-based approach to visual disambiguation, formulating it as a binary classification task on image pairs. To that end, we introduce a new dataset for this problem, Doppelgangers, which includes image pairs of similar structures with ground truth labels. We also design a network architecture that takes the spatial distribution of local keypoints and matches as input, allowing for better reasoning about both local and global cues. Our evaluation shows that our method can distinguish illusory matches in difficult cases, and can be integrated into SfM pipelines to produce correct, disambiguated 3D reconstructions. See our project page for our code, datasets, and more results: http://doppelgangers-3d.github.io/. |
|
2023-09-06T00:00:00 | 2309.02186 | AniPortraitGAN: Animatable 3D Portrait Generation from 2D Image Collections | [
"Yue Wu",
"Sicheng Xu",
"Jianfeng Xiang",
"Fangyun Wei",
"Qifeng Chen",
"Jiaolong Yang",
"Xin Tong"
] | Previous animatable 3D-aware GANs for human generation have primarily focused on either the human head or full body. However, head-only videos are relatively uncommon in real life, and full body generation typically does not deal with facial expression control and still has challenges in generating high-quality results. Towards applicable video avatars, we present an animatable 3D-aware GAN that generates portrait images with controllable facial expression, head pose, and shoulder movements. It is a generative model trained on unstructured 2D image collections without using 3D or video data. For the new task, we base our method on the generative radiance manifold representation and equip it with learnable facial and head-shoulder deformations. A dual-camera rendering and adversarial learning scheme is proposed to improve the quality of the generated faces, which is critical for portrait images. A pose deformation processing network is developed to generate plausible deformations for challenging regions such as long hair. Experiments show that our method, trained on unstructured 2D images, can generate diverse and high-quality 3D portraits with desired control over different properties. |
|
2023-09-06T00:00:00 | 2309.00987 | Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon Manipulation | [
"Yuanpei Chen",
"Chen Wang",
"Li Fei-Fei",
"C. Karen Liu"
] | Many real-world manipulation tasks consist of a series of subtasks that are significantly different from one another. Such long-horizon, complex tasks highlight the potential of dexterous hands, which possess adaptability and versatility, capable of seamlessly transitioning between different modes of functionality without the need for re-grasping or external tools. However, the challenges arise due to the high-dimensional action space of dexterous hand and complex compositional dynamics of the long-horizon tasks. We present Sequential Dexterity, a general system based on reinforcement learning (RL) that chains multiple dexterous policies for achieving long-horizon task goals. The core of the system is a transition feasibility function that progressively finetunes the sub-policies for enhancing chaining success rate, while also enables autonomous policy-switching for recovery from failures and bypassing redundant stages. Despite being trained only in simulation with a few task objects, our system demonstrates generalization capability to novel object shapes and is able to zero-shot transfer to a real-world robot equipped with a dexterous hand. More details and video results could be found at https://sequential-dexterity.github.io |
|
2023-09-06T00:00:00 | 2309.01775 | Gated recurrent neural networks discover attention | [
"Nicolas Zucchet",
"Seijin Kobayashi",
"Yassir Akram",
"Johannes von Oswald",
"Maxime Larcher",
"Angelika Steger",
"João Sacramento"
] | Recent architectural developments have enabled recurrent neural networks (RNNs) to reach and even surpass the performance of Transformers on certain sequence modeling tasks. These modern RNNs feature a prominent design pattern: linear recurrent layers interconnected by feedforward paths with multiplicative gating. Here, we show how RNNs equipped with these two design elements can exactly implement (linear) self-attention, the main building block of Transformers. By reverse-engineering a set of trained RNNs, we find that gradient descent in practice discovers our construction. In particular, we examine RNNs trained to solve simple in-context learning tasks on which Transformers are known to excel and find that gradient descent instills in our RNNs the same attention-based in-context learning algorithm used by Transformers. Our findings highlight the importance of multiplicative interactions in neural networks and suggest that certain RNNs might be unexpectedly implementing attention under the hood. |
|
2023-09-07T00:00:00 | 2309.03179 | SLiMe: Segment Like Me | [
"Aliasghar Khani",
"Saeid Asgari Taghanaki",
"Aditya Sanghi",
"Ali Mahdavi Amiri",
"Ghassan Hamarneh"
] | Significant strides have been made using large vision-language models, like Stable Diffusion (SD), for a variety of downstream tasks, including image editing, image correspondence, and 3D shape generation. Inspired by these advancements, we explore leveraging these extensive vision-language models for segmenting images at any desired granularity using as few as one annotated sample by proposing SLiMe. SLiMe frames this problem as an optimization task. Specifically, given a single training image and its segmentation mask, we first extract attention maps, including our novel "weighted accumulated self-attention map" from the SD prior. Then, using the extracted attention maps, the text embeddings of Stable Diffusion are optimized such that, each of them, learn about a single segmented region from the training image. These learned embeddings then highlight the segmented region in the attention maps, which in turn can then be used to derive the segmentation map. This enables SLiMe to segment any real-world image during inference with the granularity of the segmented region in the training image, using just one example. Moreover, leveraging additional training data when available, i.e. few-shot, improves the performance of SLiMe. We carried out a knowledge-rich set of experiments examining various design factors and showed that SLiMe outperforms other existing one-shot and few-shot segmentation methods. |
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2023-09-07T00:00:00 | 2309.03160 | ResFields: Residual Neural Fields for Spatiotemporal Signals | [
"Marko Mihajlovic",
"Sergey Prokudin",
"Marc Pollefeys",
"Siyu Tang"
] | Neural fields, a category of neural networks trained to represent high-frequency signals, have gained significant attention in recent years due to their impressive performance in modeling complex 3D data, especially large neural signed distance (SDFs) or radiance fields (NeRFs) via a single multi-layer perceptron (MLP). However, despite the power and simplicity of representing signals with an MLP, these methods still face challenges when modeling large and complex temporal signals due to the limited capacity of MLPs. In this paper, we propose an effective approach to address this limitation by incorporating temporal residual layers into neural fields, dubbed ResFields, a novel class of networks specifically designed to effectively represent complex temporal signals. We conduct a comprehensive analysis of the properties of ResFields and propose a matrix factorization technique to reduce the number of trainable parameters and enhance generalization capabilities. Importantly, our formulation seamlessly integrates with existing techniques and consistently improves results across various challenging tasks: 2D video approximation, dynamic shape modeling via temporal SDFs, and dynamic NeRF reconstruction. Lastly, we demonstrate the practical utility of ResFields by showcasing its effectiveness in capturing dynamic 3D scenes from sparse sensory inputs of a lightweight capture system. |
|
2023-09-07T00:00:00 | 2309.03130 | MyoDex: A Generalizable Prior for Dexterous Manipulation | [
"Vittorio Caggiano",
"Sudeep Dasari",
"Vikash Kumar"
] | Human dexterity is a hallmark of motor control. Our hands can rapidly synthesize new behaviors despite the complexity (multi-articular and multi-joints, with 23 joints controlled by more than 40 muscles) of musculoskeletal sensory-motor circuits. In this work, we take inspiration from how human dexterity builds on a diversity of prior experiences, instead of being acquired through a single task. Motivated by this observation, we set out to develop agents that can build upon their previous experience to quickly acquire new (previously unattainable) behaviors. Specifically, our approach leverages multi-task learning to implicitly capture task-agnostic behavioral priors (MyoDex) for human-like dexterity, using a physiologically realistic human hand model - MyoHand. We demonstrate MyoDex's effectiveness in few-shot generalization as well as positive transfer to a large repertoire of unseen dexterous manipulation tasks. Agents leveraging MyoDex can solve approximately 3x more tasks, and 4x faster in comparison to a distillation baseline. While prior work has synthesized single musculoskeletal control behaviors, MyoDex is the first generalizable manipulation prior that catalyzes the learning of dexterous physiological control across a large variety of contact-rich behaviors. We also demonstrate the effectiveness of our paradigms beyond musculoskeletal control towards the acquisition of dexterity in 24 DoF Adroit Hand. Website: https://sites.google.com/view/myodex |
|
2023-09-07T00:00:00 | 2309.02591 | Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning | [
"Lili Yu",
"Bowen Shi",
"Ramakanth Pasunuru",
"Benjamin Muller",
"Olga Golovneva",
"Tianlu Wang",
"Arun Babu",
"Binh Tang",
"Brian Karrer",
"Shelly Sheynin",
"Candace Ross",
"Adam Polyak",
"Russell Howes",
"Vasu Sharma",
"Puxin Xu",
"Hovhannes Tamoyan",
"Oron Ashual",
"Uriel Singer",
"Shang-Wen Li",
"Susan Zhang",
"Richard James",
"Gargi Ghosh",
"Yaniv Taigman",
"Maryam Fazel-Zarandi",
"Asli Celikyilmaz",
"Luke Zettlemoyer",
"Armen Aghajanyan"
] | We present CM3Leon (pronounced "Chameleon"), a retrieval-augmented, token-based, decoder-only multi-modal language model capable of generating and infilling both text and images. CM3Leon uses the CM3 multi-modal architecture but additionally shows the extreme benefits of scaling up and tuning on more diverse instruction-style data. It is the first multi-modal model trained with a recipe adapted from text-only language models, including a large-scale retrieval-augmented pre-training stage and a second multi-task supervised fine-tuning (SFT) stage. It is also a general-purpose model that can do both text-to-image and image-to-text generation, allowing us to introduce self-contained contrastive decoding methods that produce high-quality outputs. Extensive experiments demonstrate that this recipe is highly effective for multi-modal models. CM3Leon achieves state-of-the-art performance in text-to-image generation with 5x less training compute than comparable methods (zero-shot MS-COCO FID of 4.88). After SFT, CM3Leon can also demonstrate unprecedented levels of controllability in tasks ranging from language-guided image editing to image-controlled generation and segmentation. |
|
2023-09-07T00:00:00 | 2309.02561 | Physically Grounded Vision-Language Models for Robotic Manipulation | [
"Jensen Gao",
"Bidipta Sarkar",
"Fei Xia",
"Ted Xiao",
"Jiajun Wu",
"Brian Ichter",
"Anirudha Majumdar",
"Dorsa Sadigh"
] | Recent advances in vision-language models (VLMs) have led to improved performance on tasks such as visual question answering and image captioning. Consequently, these models are now well-positioned to reason about the physical world, particularly within domains such as robotic manipulation. However, current VLMs are limited in their understanding of the physical concepts (e.g., material, fragility) of common objects, which restricts their usefulness for robotic manipulation tasks that involve interaction and physical reasoning about such objects. To address this limitation, we propose PhysObjects, an object-centric dataset of 36.9K crowd-sourced and 417K automated physical concept annotations of common household objects. We demonstrate that fine-tuning a VLM on PhysObjects improves its understanding of physical object concepts, by capturing human priors of these concepts from visual appearance. We incorporate this physically-grounded VLM in an interactive framework with a large language model-based robotic planner, and show improved planning performance on tasks that require reasoning about physical object concepts, compared to baselines that do not leverage physically-grounded VLMs. We additionally illustrate the benefits of our physically-grounded VLM on a real robot, where it improves task success rates. We release our dataset and provide further details and visualizations of our results at https://iliad.stanford.edu/pg-vlm/. |
|
2023-09-07T00:00:00 | 2309.03185 | Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields | [
"Lily Goli",
"Cody Reading",
"Silvia Selllán",
"Alec Jacobson",
"Andrea Tagliasacchi"
] | Neural Radiance Fields (NeRFs) have shown promise in applications like view synthesis and depth estimation, but learning from multiview images faces inherent uncertainties. Current methods to quantify them are either heuristic or computationally demanding. We introduce BayesRays, a post-hoc framework to evaluate uncertainty in any pre-trained NeRF without modifying the training process. Our method establishes a volumetric uncertainty field using spatial perturbations and a Bayesian Laplace approximation. We derive our algorithm statistically and show its superior performance in key metrics and applications. Additional results available at: https://bayesrays.github.io. |
|
2023-09-07T00:00:00 | 2309.03199 | Matcha-TTS: A fast TTS architecture with conditional flow matching | [
"Shivam Mehta",
"Ruibo Tu",
"Jonas Beskow",
"Éva Székely",
"Gustav Eje Henter"
] | We introduce Matcha-TTS, a new encoder-decoder architecture for speedy TTS acoustic modelling, trained using optimal-transport conditional flow matching (OT-CFM). This yields an ODE-based decoder capable of high output quality in fewer synthesis steps than models trained using score matching. Careful design choices additionally ensure each synthesis step is fast to run. The method is probabilistic, non-autoregressive, and learns to speak from scratch without external alignments. Compared to strong pre-trained baseline models, the Matcha-TTS system has the smallest memory footprint, rivals the speed of the fastest models on long utterances, and attains the highest mean opinion score in a listening test. Please see https://shivammehta25.github.io/Matcha-TTS/ for audio examples, code, and pre-trained models. |
|
2023-09-08T00:00:00 | 2309.03409 | Large Language Models as Optimizers | [
"Chengrun Yang",
"Xuezhi Wang",
"Yifeng Lu",
"Hanxiao Liu",
"Quoc V. Le",
"Denny Zhou",
"Xinyun Chen"
] | Optimization is ubiquitous. While derivative-based algorithms have been powerful tools for various problems, the absence of gradient imposes challenges on many real-world applications. In this work, we propose Optimization by PROmpting (OPRO), a simple and effective approach to leverage large language models (LLMs) as optimizers, where the optimization task is described in natural language. In each optimization step, the LLM generates new solutions from the prompt that contains previously generated solutions with their values, then the new solutions are evaluated and added to the prompt for the next optimization step. We first showcase OPRO on linear regression and traveling salesman problems, then move on to prompt optimization where the goal is to find instructions that maximize the task accuracy. With a variety of LLMs, we demonstrate that the best prompts optimized by OPRO outperform human-designed prompts by up to 8% on GSM8K, and by up to 50% on Big-Bench Hard tasks. |
|
2023-09-08T00:00:00 | 2309.03905 | ImageBind-LLM: Multi-modality Instruction Tuning | [
"Jiaming Han",
"Renrui Zhang",
"Wenqi Shao",
"Peng Gao",
"Peng Xu",
"Han Xiao",
"Kaipeng Zhang",
"Chris Liu",
"Song Wen",
"Ziyu Guo",
"Xudong Lu",
"Shuai Ren",
"Yafei Wen",
"Xiaoxin Chen",
"Xiangyu Yue",
"Hongsheng Li",
"Yu Qiao"
] | https://github.com/OpenGVLab/LLaMA-Adapter | We present ImageBind-LLM, a multi-modality instruction tuning method of large language models (LLMs) via ImageBind. Existing works mainly focus on language and image instruction tuning, different from which, our ImageBind-LLM can respond to multi-modality conditions, including audio, 3D point clouds, video, and their embedding-space arithmetic by only image-text alignment training. During training, we adopt a learnable bind network to align the embedding space between LLaMA and ImageBind's image encoder. Then, the image features transformed by the bind network are added to word tokens of all layers in LLaMA, which progressively injects visual instructions via an attention-free and zero-initialized gating mechanism. Aided by the joint embedding of ImageBind, the simple image-text training enables our model to exhibit superior multi-modality instruction-following capabilities. During inference, the multi-modality inputs are fed into the corresponding ImageBind encoders, and processed by a proposed visual cache model for further cross-modal embedding enhancement. The training-free cache model retrieves from three million image features extracted by ImageBind, which effectively mitigates the training-inference modality discrepancy. Notably, with our approach, ImageBind-LLM can respond to instructions of diverse modalities and demonstrate significant language generation quality. Code is released at https://github.com/OpenGVLab/LLaMA-Adapter. |
2023-09-08T00:00:00 | 2309.03852 | FLM-101B: An Open LLM and How to Train It with $100K Budget | [
"Xiang Li",
"Yiqun Yao",
"Xin Jiang",
"Xuezhi Fang",
"Xuying Meng",
"Siqi Fan",
"Peng Han",
"Jing Li",
"Li Du",
"Bowen Qin",
"Zheng Zhang",
"Aixin Sun",
"Yequan Wang"
] | Large language models (LLMs) have achieved remarkable success in NLP and multimodal tasks. Despite these successes, their development faces two main challenges: (i) high computational cost; and (ii) difficulty in conducting fair and objective evaluations. LLMs are prohibitively expensive, making it feasible for only a few major players to undertake their training, thereby constraining both research and application opportunities. This underscores the importance of cost-effective LLM training. In this paper, we utilize a growth strategy to significantly reduce LLM training cost. We demonstrate that an LLM with 101B parameters and 0.31TB tokens can be trained on a 100K budget. We also adopt a systematic evaluation paradigm for the IQ evaluation of LLMs, in complement to existing evaluations that focus more on knowledge-oriented abilities. We introduce our benchmark including evaluations on important aspects of intelligence including symbolic mapping, itrule understanding, pattern mining, and anti-interference. Such evaluations minimize the potential impact of memorization. Experimental results show that our model FLM-101B, trained with a budget of 100K, achieves comparable performance to powerful and well-known models, eg GPT-3 and GLM-130B, especially in the IQ benchmark evaluations with contexts unseen in training data. The checkpoint of FLM-101B will be open-sourced at https://huggingface.co/CofeAI/FLM-101B. |
|
2023-09-08T00:00:00 | 2309.03241 | GPT Can Solve Mathematical Problems Without a Calculator | [
"Zhen Yang",
"Ming Ding",
"Qingsong Lv",
"Zhihuan Jiang",
"Zehai He",
"Yuyi Guo",
"Jinfeng Bai",
"Jie Tang"
] | Previous studies have typically assumed that large language models are unable to accurately perform arithmetic operations, particularly multiplication of >8 digits, and operations involving decimals and fractions, without the use of calculator tools. This paper aims to challenge this misconception. With sufficient training data, a 2 billion-parameter language model can accurately perform multi-digit arithmetic operations with almost 100% accuracy without data leakage, significantly surpassing GPT-4 (whose multi-digit multiplication accuracy is only 4.3%). We also demonstrate that our MathGLM, fine-tuned from GLM-10B on a dataset with additional multi-step arithmetic operations and math problems described in text, achieves similar performance to GPT-4 on a 5,000-samples Chinese math problem test set. |
|
2023-09-08T00:00:00 | 2309.03549 | Reuse and Diffuse: Iterative Denoising for Text-to-Video Generation | [
"Jiaxi Gu",
"Shicong Wang",
"Haoyu Zhao",
"Tianyi Lu",
"Xing Zhang",
"Zuxuan Wu",
"Songcen Xu",
"Wei Zhang",
"Yu-Gang Jiang",
"Hang Xu"
] | Inspired by the remarkable success of Latent Diffusion Models (LDMs) for image synthesis, we study LDM for text-to-video generation, which is a formidable challenge due to the computational and memory constraints during both model training and inference. A single LDM is usually only capable of generating a very limited number of video frames. Some existing works focus on separate prediction models for generating more video frames, which suffer from additional training cost and frame-level jittering, however. In this paper, we propose a framework called "Reuse and Diffuse" dubbed VidRD to produce more frames following the frames already generated by an LDM. Conditioned on an initial video clip with a small number of frames, additional frames are iteratively generated by reusing the original latent features and following the previous diffusion process. Besides, for the autoencoder used for translation between pixel space and latent space, we inject temporal layers into its decoder and fine-tune these layers for higher temporal consistency. We also propose a set of strategies for composing video-text data that involve diverse content from multiple existing datasets including video datasets for action recognition and image-text datasets. Extensive experiments show that our method achieves good results in both quantitative and qualitative evaluations. Our project page is available https://anonymous0x233.github.io/ReuseAndDiffuse/{here}. |
|
2023-09-08T00:00:00 | 2309.03550 | Text2Control3D: Controllable 3D Avatar Generation in Neural Radiance Fields using Geometry-Guided Text-to-Image Diffusion Model | [
"Sungwon Hwang",
"Junha Hyung",
"Jaegul Choo"
] | Recent advances in diffusion models such as ControlNet have enabled geometrically controllable, high-fidelity text-to-image generation. However, none of them addresses the question of adding such controllability to text-to-3D generation. In response, we propose Text2Control3D, a controllable text-to-3D avatar generation method whose facial expression is controllable given a monocular video casually captured with hand-held camera. Our main strategy is to construct the 3D avatar in Neural Radiance Fields (NeRF) optimized with a set of controlled viewpoint-aware images that we generate from ControlNet, whose condition input is the depth map extracted from the input video. When generating the viewpoint-aware images, we utilize cross-reference attention to inject well-controlled, referential facial expression and appearance via cross attention. We also conduct low-pass filtering of Gaussian latent of the diffusion model in order to ameliorate the viewpoint-agnostic texture problem we observed from our empirical analysis, where the viewpoint-aware images contain identical textures on identical pixel positions that are incomprehensible in 3D. Finally, to train NeRF with the images that are viewpoint-aware yet are not strictly consistent in geometry, our approach considers per-image geometric variation as a view of deformation from a shared 3D canonical space. Consequently, we construct the 3D avatar in a canonical space of deformable NeRF by learning a set of per-image deformation via deformation field table. We demonstrate the empirical results and discuss the effectiveness of our method. |
|
2023-09-08T00:00:00 | 2309.03883 | DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models | [
"Yung-Sung Chuang",
"Yujia Xie",
"Hongyin Luo",
"Yoon Kim",
"James Glass",
"Pengcheng He"
] | Despite their impressive capabilities, large language models (LLMs) are prone to hallucinations, i.e., generating content that deviates from facts seen during pretraining. We propose a simple decoding strategy for reducing hallucinations with pretrained LLMs that does not require conditioning on retrieved external knowledge nor additional fine-tuning. Our approach obtains the next-token distribution by contrasting the differences in logits obtained from projecting the later layers versus earlier layers to the vocabulary space, exploiting the fact that factual knowledge in an LLMs has generally been shown to be localized to particular transformer layers. We find that this Decoding by Contrasting Layers (DoLa) approach is able to better surface factual knowledge and reduce the generation of incorrect facts. DoLa consistently improves the truthfulness across multiple choices tasks and open-ended generation tasks, for example improving the performance of LLaMA family models on TruthfulQA by 12-17% absolute points, demonstrating its potential in making LLMs reliably generate truthful facts. |
|
2023-09-08T00:00:00 | 2309.03450 | XGen-7B Technical Report | [
"Erik Nijkamp",
"Tian Xie",
"Hiroaki Hayashi",
"Bo Pang",
"Congying Xia",
"Chen Xing",
"Jesse Vig",
"Semih Yavuz",
"Philippe Laban",
"Ben Krause",
"Senthil Purushwalkam",
"Tong Niu",
"Wojciech Kryściński",
"Lidiya Murakhovs'ka",
"Prafulla Kumar Choubey",
"Alex Fabbri",
"Ye Liu",
"Rui Meng",
"Lifu Tu",
"Meghana Bhat",
"Chien-Sheng Wu",
"Silvio Savarese",
"Yingbo Zhou",
"Shafiq Joty",
"Caiming Xiong"
] | Large Language Models (LLMs) have become ubiquitous across various domains, transforming the way we interact with information and conduct research. However, most high-performing LLMs remain confined behind proprietary walls, hindering scientific progress. Most open-source LLMs, on the other hand, are limited in their ability to support longer sequence lengths, which is a key requirement for many tasks that require inference over an input context. To address this, we have trained XGen, a series of 7B parameter models on up to 8K sequence length for up to 1.5T tokens. We have also finetuned the XGen models on public-domain instructional data, creating their instruction-tuned counterparts (XGen-Inst). We open-source our models for both research advancements and commercial applications. Our evaluation on standard benchmarks shows that XGen models achieve comparable or better results when compared with state-of-the-art open-source LLMs. Our targeted evaluation on long sequence modeling tasks shows the benefits of our 8K-sequence models over 2K-sequence open-source LLMs. |
|
2023-09-08T00:00:00 | 2309.03903 | Tracking Anything with Decoupled Video Segmentation | [
"Ho Kei Cheng",
"Seoung Wug Oh",
"Brian Price",
"Alexander Schwing",
"Joon-Young Lee"
] | Training data for video segmentation are expensive to annotate. This impedes extensions of end-to-end algorithms to new video segmentation tasks, especially in large-vocabulary settings. To 'track anything' without training on video data for every individual task, we develop a decoupled video segmentation approach (DEVA), composed of task-specific image-level segmentation and class/task-agnostic bi-directional temporal propagation. Due to this design, we only need an image-level model for the target task (which is cheaper to train) and a universal temporal propagation model which is trained once and generalizes across tasks. To effectively combine these two modules, we use bi-directional propagation for (semi-)online fusion of segmentation hypotheses from different frames to generate a coherent segmentation. We show that this decoupled formulation compares favorably to end-to-end approaches in several data-scarce tasks including large-vocabulary video panoptic segmentation, open-world video segmentation, referring video segmentation, and unsupervised video object segmentation. Code is available at: https://hkchengrex.github.io/Tracking-Anything-with-DEVA |
|
2023-09-08T00:00:00 | 2309.03315 | Robotic Table Tennis: A Case Study into a High Speed Learning System | [
"David B. D'Ambrosio",
"Jonathan Abelian",
"Saminda Abeyruwan",
"Michael Ahn",
"Alex Bewley",
"Justin Boyd",
"Krzysztof Choromanski",
"Omar Cortes",
"Erwin Coumans",
"Tianli Ding",
"Wenbo Gao",
"Laura Graesser",
"Atil Iscen",
"Navdeep Jaitly",
"Deepali Jain",
"Juhana Kangaspunta",
"Satoshi Kataoka",
"Gus Kouretas",
"Yuheng Kuang",
"Nevena Lazic",
"Corey Lynch",
"Reza Mahjourian",
"Sherry Q. Moore",
"Thinh Nguyen",
"Ken Oslund",
"Barney J Reed",
"Krista Reymann",
"Pannag R. Sanketi",
"Anish Shankar",
"Pierre Sermanet",
"Vikas Sindhwani",
"Avi Singh",
"Vincent Vanhoucke",
"Grace Vesom",
"Peng Xu"
] | We present a deep-dive into a real-world robotic learning system that, in previous work, was shown to be capable of hundreds of table tennis rallies with a human and has the ability to precisely return the ball to desired targets. This system puts together a highly optimized perception subsystem, a high-speed low-latency robot controller, a simulation paradigm that can prevent damage in the real world and also train policies for zero-shot transfer, and automated real world environment resets that enable autonomous training and evaluation on physical robots. We complement a complete system description, including numerous design decisions that are typically not widely disseminated, with a collection of studies that clarify the importance of mitigating various sources of latency, accounting for training and deployment distribution shifts, robustness of the perception system, sensitivity to policy hyper-parameters, and choice of action space. A video demonstrating the components of the system and details of experimental results can be found at https://youtu.be/uFcnWjB42I0. |
|
2023-09-08T00:00:00 | 2309.03897 | ProPainter: Improving Propagation and Transformer for Video Inpainting | [
"Shangchen Zhou",
"Chongyi Li",
"Kelvin C. K. Chan",
"Chen Change Loy"
] | https://github.com/sczhou/ProPainter | Flow-based propagation and spatiotemporal Transformer are two mainstream mechanisms in video inpainting (VI). Despite the effectiveness of these components, they still suffer from some limitations that affect their performance. Previous propagation-based approaches are performed separately either in the image or feature domain. Global image propagation isolated from learning may cause spatial misalignment due to inaccurate optical flow. Moreover, memory or computational constraints limit the temporal range of feature propagation and video Transformer, preventing exploration of correspondence information from distant frames. To address these issues, we propose an improved framework, called ProPainter, which involves enhanced ProPagation and an efficient Transformer. Specifically, we introduce dual-domain propagation that combines the advantages of image and feature warping, exploiting global correspondences reliably. We also propose a mask-guided sparse video Transformer, which achieves high efficiency by discarding unnecessary and redundant tokens. With these components, ProPainter outperforms prior arts by a large margin of 1.46 dB in PSNR while maintaining appealing efficiency. |
2023-09-08T00:00:00 | 2309.03895 | InstructDiffusion: A Generalist Modeling Interface for Vision Tasks | [
"Zigang Geng",
"Binxin Yang",
"Tiankai Hang",
"Chen Li",
"Shuyang Gu",
"Ting Zhang",
"Jianmin Bao",
"Zheng Zhang",
"Han Hu",
"Dong Chen",
"Baining Guo"
] | https://github.com/cientgu/InstructDiffusion | We present InstructDiffusion, a unifying and generic framework for aligning computer vision tasks with human instructions. Unlike existing approaches that integrate prior knowledge and pre-define the output space (e.g., categories and coordinates) for each vision task, we cast diverse vision tasks into a human-intuitive image-manipulating process whose output space is a flexible and interactive pixel space. Concretely, the model is built upon the diffusion process and is trained to predict pixels according to user instructions, such as encircling the man's left shoulder in red or applying a blue mask to the left car. InstructDiffusion could handle a variety of vision tasks, including understanding tasks (such as segmentation and keypoint detection) and generative tasks (such as editing and enhancement). It even exhibits the ability to handle unseen tasks and outperforms prior methods on novel datasets. This represents a significant step towards a generalist modeling interface for vision tasks, advancing artificial general intelligence in the field of computer vision. |
2023-09-08T00:00:00 | 2309.03453 | SyncDreamer: Generating Multiview-consistent Images from a Single-view Image | [
"Yuan Liu",
"Cheng Lin",
"Zijiao Zeng",
"Xiaoxiao Long",
"Lingjie Liu",
"Taku Komura",
"Wenping Wang"
] | https://github.com/liuyuan-pal/SyncDreamer | In this paper, we present a novel diffusion model called that generates multiview-consistent images from a single-view image. Using pretrained large-scale 2D diffusion models, recent work Zero123 demonstrates the ability to generate plausible novel views from a single-view image of an object. However, maintaining consistency in geometry and colors for the generated images remains a challenge. To address this issue, we propose a synchronized multiview diffusion model that models the joint probability distribution of multiview images, enabling the generation of multiview-consistent images in a single reverse process. SyncDreamer synchronizes the intermediate states of all the generated images at every step of the reverse process through a 3D-aware feature attention mechanism that correlates the corresponding features across different views. Experiments show that SyncDreamer generates images with high consistency across different views, thus making it well-suited for various 3D generation tasks such as novel-view-synthesis, text-to-3D, and image-to-3D. |
2023-09-11T00:00:00 | 2309.04269 | From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting | [
"Griffin Adams",
"Alexander Fabbri",
"Faisal Ladhak",
"Eric Lehman",
"Noémie Elhadad"
] | Selecting the ``right'' amount of information to include in a summary is a difficult task. A good summary should be detailed and entity-centric without being overly dense and hard to follow. To better understand this tradeoff, we solicit increasingly dense GPT-4 summaries with what we refer to as a ``Chain of Density'' (CoD) prompt. Specifically, GPT-4 generates an initial entity-sparse summary before iteratively incorporating missing salient entities without increasing the length. Summaries generated by CoD are more abstractive, exhibit more fusion, and have less of a lead bias than GPT-4 summaries generated by a vanilla prompt. We conduct a human preference study on 100 CNN DailyMail articles and find that that humans prefer GPT-4 summaries that are more dense than those generated by a vanilla prompt and almost as dense as human written summaries. Qualitative analysis supports the notion that there exists a tradeoff between informativeness and readability. 500 annotated CoD summaries, as well as an extra 5,000 unannotated summaries, are freely available on HuggingFace (https://huggingface.co/datasets/griffin/chain_of_density). |
|
2023-09-11T00:00:00 | 2309.03926 | Large-Scale Automatic Audiobook Creation | [
"Brendan Walsh",
"Mark Hamilton",
"Greg Newby",
"Xi Wang",
"Serena Ruan",
"Sheng Zhao",
"Lei He",
"Shaofei Zhang",
"Eric Dettinger",
"William T. Freeman",
"Markus Weimer"
] | An audiobook can dramatically improve a work of literature's accessibility and improve reader engagement. However, audiobooks can take hundreds of hours of human effort to create, edit, and publish. In this work, we present a system that can automatically generate high-quality audiobooks from online e-books. In particular, we leverage recent advances in neural text-to-speech to create and release thousands of human-quality, open-license audiobooks from the Project Gutenberg e-book collection. Our method can identify the proper subset of e-book content to read for a wide collection of diversely structured books and can operate on hundreds of books in parallel. Our system allows users to customize an audiobook's speaking speed and style, emotional intonation, and can even match a desired voice using a small amount of sample audio. This work contributed over five thousand open-license audiobooks and an interactive demo that allows users to quickly create their own customized audiobooks. To listen to the audiobook collection visit https://aka.ms/audiobook. |
|
2023-09-11T00:00:00 | 2309.04247 | Towards Practical Capture of High-Fidelity Relightable Avatars | [
"Haotian Yang",
"Mingwu Zheng",
"Wanquan Feng",
"Haibin Huang",
"Yu-Kun Lai",
"Pengfei Wan",
"Zhongyuan Wang",
"Chongyang Ma"
] | In this paper, we propose a novel framework, Tracking-free Relightable Avatar (TRAvatar), for capturing and reconstructing high-fidelity 3D avatars. Compared to previous methods, TRAvatar works in a more practical and efficient setting. Specifically, TRAvatar is trained with dynamic image sequences captured in a Light Stage under varying lighting conditions, enabling realistic relighting and real-time animation for avatars in diverse scenes. Additionally, TRAvatar allows for tracking-free avatar capture and obviates the need for accurate surface tracking under varying illumination conditions. Our contributions are two-fold: First, we propose a novel network architecture that explicitly builds on and ensures the satisfaction of the linear nature of lighting. Trained on simple group light captures, TRAvatar can predict the appearance in real-time with a single forward pass, achieving high-quality relighting effects under illuminations of arbitrary environment maps. Second, we jointly optimize the facial geometry and relightable appearance from scratch based on image sequences, where the tracking is implicitly learned. This tracking-free approach brings robustness for establishing temporal correspondences between frames under different lighting conditions. Extensive qualitative and quantitative experiments demonstrate that our framework achieves superior performance for photorealistic avatar animation and relighting. |
|
2023-09-11T00:00:00 | 2309.04354 | Mobile V-MoEs: Scaling Down Vision Transformers via Sparse Mixture-of-Experts | [
"Erik Daxberger",
"Floris Weers",
"Bowen Zhang",
"Tom Gunter",
"Ruoming Pang",
"Marcin Eichner",
"Michael Emmersberger",
"Yinfei Yang",
"Alexander Toshev",
"Xianzhi Du"
] | Sparse Mixture-of-Experts models (MoEs) have recently gained popularity due to their ability to decouple model size from inference efficiency by only activating a small subset of the model parameters for any given input token. As such, sparse MoEs have enabled unprecedented scalability, resulting in tremendous successes across domains such as natural language processing and computer vision. In this work, we instead explore the use of sparse MoEs to scale-down Vision Transformers (ViTs) to make them more attractive for resource-constrained vision applications. To this end, we propose a simplified and mobile-friendly MoE design where entire images rather than individual patches are routed to the experts. We also propose a stable MoE training procedure that uses super-class information to guide the router. We empirically show that our sparse Mobile Vision MoEs (V-MoEs) can achieve a better trade-off between performance and efficiency than the corresponding dense ViTs. For example, for the ViT-Tiny model, our Mobile V-MoE outperforms its dense counterpart by 3.39% on ImageNet-1k. For an even smaller ViT variant with only 54M FLOPs inference cost, our MoE achieves an improvement of 4.66%. |
|
2023-09-11T00:00:00 | 2309.03907 | DrugChat: Towards Enabling ChatGPT-Like Capabilities on Drug Molecule Graphs | [
"Youwei Liang",
"Ruiyi Zhang",
"Li Zhang",
"Pengtao Xie"
] | https://github.com/UCSD-AI4H/drugchat | A ChatGPT-like system for drug compounds could be a game-changer in pharmaceutical research, accelerating drug discovery, enhancing our understanding of structure-activity relationships, guiding lead optimization, aiding drug repurposing, reducing the failure rate, and streamlining clinical trials. In this work, we make an initial attempt towards enabling ChatGPT-like capabilities on drug molecule graphs, by developing a prototype system DrugChat. DrugChat works in a similar way as ChatGPT. Users upload a compound molecule graph and ask various questions about this compound. DrugChat will answer these questions in a multi-turn, interactive manner. The DrugChat system consists of a graph neural network (GNN), a large language model (LLM), and an adaptor. The GNN takes a compound molecule graph as input and learns a representation for this graph. The adaptor transforms the graph representation produced by the GNN into another representation that is acceptable to the LLM. The LLM takes the compound representation transformed by the adaptor and users' questions about this compound as inputs and generates answers. All these components are trained end-to-end. To train DrugChat, we collected instruction tuning datasets which contain 10,834 drug compounds and 143,517 question-answer pairs. The code and data is available at https://github.com/UCSD-AI4H/drugchat |
2023-09-12T00:00:00 | 2309.05519 | NExT-GPT: Any-to-Any Multimodal LLM | [
"Shengqiong Wu",
"Hao Fei",
"Leigang Qu",
"Wei Ji",
"Tat-Seng Chua"
] | While recently Multimodal Large Language Models (MM-LLMs) have made exciting strides, they mostly fall prey to the limitation of only input-side multimodal understanding, without the ability to produce content in multiple modalities. As we humans always perceive the world and communicate with people through various modalities, developing any-to-any MM-LLMs capable of accepting and delivering content in any modality becomes essential to human-level AI. To fill the gap, we present an end-to-end general-purpose any-to-any MM-LLM system, NExT-GPT. We connect an LLM with multimodal adaptors and different diffusion decoders, enabling NExT-GPT to perceive inputs and generate outputs in arbitrary combinations of text, images, videos, and audio. By leveraging the existing well-trained highly-performing encoders and decoders, NExT-GPT is tuned with only a small amount of parameter (1%) of certain projection layers, which not only benefits low-cost training and also facilitates convenient expansion to more potential modalities. Moreover, we introduce a modality-switching instruction tuning (MosIT) and manually curate a high-quality dataset for MosIT, based on which NExT-GPT is empowered with complex cross-modal semantic understanding and content generation. Overall, our research showcases the promising possibility of building an AI agent capable of modeling universal modalities, paving the way for more human-like AI research in the community. |
|
2023-09-12T00:00:00 | 2309.05463 | Textbooks Are All You Need II: phi-1.5 technical report | [
"Yuanzhi Li",
"Sébastien Bubeck",
"Ronen Eldan",
"Allie Del Giorno",
"Suriya Gunasekar",
"Yin Tat Lee"
] | We continue the investigation into the power of smaller Transformer-based language models as initiated by TinyStories -- a 10 million parameter model that can produce coherent English -- and the follow-up work on phi-1, a 1.3 billion parameter model with Python coding performance close to the state-of-the-art. The latter work proposed to use existing Large Language Models (LLMs) to generate ``textbook quality" data as a way to enhance the learning process compared to traditional web data. We follow the ``Textbooks Are All You Need" approach, focusing this time on common sense reasoning in natural language, and create a new 1.3 billion parameter model named phi-1.5, with performance on natural language tasks comparable to models 5x larger, and surpassing most non-frontier LLMs on more complex reasoning tasks such as grade-school mathematics and basic coding. More generally, phi-1.5 exhibits many of the traits of much larger LLMs, both good -- such as the ability to ``think step by step" or perform some rudimentary in-context learning -- and bad, including hallucinations and the potential for toxic and biased generations -- encouragingly though, we are seeing improvement on that front thanks to the absence of web data. We open-source phi-1.5 to promote further research on these urgent topics. |
|
2023-09-12T00:00:00 | 2309.05516 | Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs | [
"Wenhua Cheng",
"Weiwei Zhang",
"Haihao Shen",
"Yiyang Cai",
"Xin He",
"Kaokao Lv"
] | https://github.com/intel/neural-compressor | Large Language Models (LLMs) have proven their exceptional capabilities in performing language-related tasks. However, their deployment poses significant challenges due to their considerable memory and storage requirements. In response to this issue, weight-only quantization, particularly 3 and 4-bit weight-only quantization, has emerged as one of the most viable solutions. As the number of bits decreases, the quantization grid broadens, thus emphasizing the importance of up and down rounding. While previous studies have demonstrated that fine-tuning up and down rounding with the addition of perturbations can enhance accuracy in some scenarios, our study is driven by the precise and limited boundary of these perturbations, where only the threshold for altering the rounding value is of significance. Consequently, we propose a concise and highly effective approach for optimizing the weight rounding task. Our method, named SignRound, involves lightweight block-wise tuning using signed gradient descent, enabling us to achieve outstanding results within 400 steps. SignRound outperforms the established baseline of rounding-to-nearest (RTN) and competes impressively against recent methods, without introducing additional inference overhead. The source code will be publicly available at https://github.com/intel/neural-compressor soon. |
2023-09-12T00:00:00 | 2309.04827 | Neurons in Large Language Models: Dead, N-gram, Positional | [
"Elena Voita",
"Javier Ferrando",
"Christoforos Nalmpantis"
] | We analyze a family of large language models in such a lightweight manner that can be done on a single GPU. Specifically, we focus on the OPT family of models ranging from 125m to 66b parameters and rely only on whether an FFN neuron is activated or not. First, we find that the early part of the network is sparse and represents many discrete features. Here, many neurons (more than 70% in some layers of the 66b model) are "dead", i.e. they never activate on a large collection of diverse data. At the same time, many of the alive neurons are reserved for discrete features and act as token and n-gram detectors. Interestingly, their corresponding FFN updates not only promote next token candidates as could be expected, but also explicitly focus on removing the information about triggering them tokens, i.e., current input. To the best of our knowledge, this is the first example of mechanisms specialized at removing (rather than adding) information from the residual stream. With scale, models become more sparse in a sense that they have more dead neurons and token detectors. Finally, some neurons are positional: them being activated or not depends largely (or solely) on position and less so (or not at all) on textual data. We find that smaller models have sets of neurons acting as position range indicators while larger models operate in a less explicit manner. |
|
2023-09-12T00:00:00 | 2309.04663 | FIAT: Fusing learning paradigms with Instruction-Accelerated Tuning | [
"Xinyi Wang",
"John Wieting",
"Jonathan H. Clark"
] | Learning paradigms for large language models (LLMs) currently tend to fall within either in-context learning (ICL) or full fine-tuning. Each of these comes with their own trade-offs based on available data, model size, compute cost, ease-of-use, and final quality with neither solution performing well across-the-board. In this article, we first describe ICL and fine-tuning paradigms in a way that highlights their natural connections. Based on these connections, we propose a new learning paradigm called FIAT that fuses the best of these paradigms together, enabling prompt-engineered instructions and chain-of-thought reasoning with the very largest models while also using similar methods to perform parameter updates on a modestly-sized LLM with parameter-efficient tuning. We evaluate FIAT's effectiveness on a variety of multilingual tasks and observe that FIAT performs better than both ICL and fine-tuning at scales ranging from 100-10,000 training examples. We hope that FIAT provides a practical way of harnessing the full potential of LLMs without needing to make a hard choice between learning paradigms. |
|
2023-09-12T00:00:00 | 2309.04662 | MADLAD-400: A Multilingual And Document-Level Large Audited Dataset | [
"Sneha Kudugunta",
"Isaac Caswell",
"Biao Zhang",
"Xavier Garcia",
"Christopher A. Choquette-Choo",
"Katherine Lee",
"Derrick Xin",
"Aditya Kusupati",
"Romi Stella",
"Ankur Bapna",
"Orhan Firat"
] | We introduce MADLAD-400, a manually audited, general domain 3T token monolingual dataset based on CommonCrawl, spanning 419 languages. We discuss the limitations revealed by self-auditing MADLAD-400, and the role data auditing had in the dataset creation process. We then train and release a 10.7B-parameter multilingual machine translation model on 250 billion tokens covering over 450 languages using publicly available data, and find that it is competitive with models that are significantly larger, and report the results on different domains. In addition, we train a 8B-parameter language model, and assess the results on few-shot translation. We make the baseline models available to the research community. |
|
2023-09-12T00:00:00 | 2309.04564 | When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale | [
"Max Marion",
"Ahmet Üstün",
"Luiza Pozzobon",
"Alex Wang",
"Marzieh Fadaee",
"Sara Hooker"
] | Large volumes of text data have contributed significantly to the development of large language models (LLMs) in recent years. This data is typically acquired by scraping the internet, leading to pretraining datasets comprised of noisy web text. To date, efforts to prune these datasets down to a higher quality subset have relied on hand-crafted heuristics encoded as rule-based filters. In this work, we take a wider view and explore scalable estimates of data quality that can be used to systematically measure the quality of pretraining data. We perform a rigorous comparison at scale of the simple data quality estimator of perplexity, as well as more sophisticated and computationally intensive estimates of the Error L2-Norm and memorization. These metrics are used to rank and prune pretraining corpora, and we subsequently compare LLMs trained on these pruned datasets. Surprisingly, we find that the simple technique of perplexity outperforms our more computationally expensive scoring methods. We improve over our no-pruning baseline while training on as little as 30% of the original training dataset. Our work sets the foundation for unexplored strategies in automatically curating high quality corpora and suggests the majority of pretraining data can be removed while retaining performance. |
|
2023-09-12T00:00:00 | 2309.04581 | Dynamic Mesh-Aware Radiance Fields | [
"Yi-Ling Qiao",
"Alexander Gao",
"Yiran Xu",
"Yue Feng",
"Jia-Bin Huang",
"Ming C. Lin"
] | Embedding polygonal mesh assets within photorealistic Neural Radience Fields (NeRF) volumes, such that they can be rendered and their dynamics simulated in a physically consistent manner with the NeRF, is under-explored from the system perspective of integrating NeRF into the traditional graphics pipeline. This paper designs a two-way coupling between mesh and NeRF during rendering and simulation. We first review the light transport equations for both mesh and NeRF, then distill them into an efficient algorithm for updating radiance and throughput along a cast ray with an arbitrary number of bounces. To resolve the discrepancy between the linear color space that the path tracer assumes and the sRGB color space that standard NeRF uses, we train NeRF with High Dynamic Range (HDR) images. We also present a strategy to estimate light sources and cast shadows on the NeRF. Finally, we consider how the hybrid surface-volumetric formulation can be efficiently integrated with a high-performance physics simulator that supports cloth, rigid and soft bodies. The full rendering and simulation system can be run on a GPU at interactive rates. We show that a hybrid system approach outperforms alternatives in visual realism for mesh insertion, because it allows realistic light transport from volumetric NeRF media onto surfaces, which affects the appearance of reflective/refractive surfaces and illumination of diffuse surfaces informed by the dynamic scene. |
|
2023-09-13T00:00:00 | 2309.05793 | PhotoVerse: Tuning-Free Image Customization with Text-to-Image Diffusion Models | [
"Li Chen",
"Mengyi Zhao",
"Yiheng Liu",
"Mingxu Ding",
"Yangyang Song",
"Shizun Wang",
"Xu Wang",
"Hao Yang",
"Jing Liu",
"Kang Du",
"Min Zheng"
] | Personalized text-to-image generation has emerged as a powerful and sought-after tool, empowering users to create customized images based on their specific concepts and prompts. However, existing approaches to personalization encounter multiple challenges, including long tuning times, large storage requirements, the necessity for multiple input images per identity, and limitations in preserving identity and editability. To address these obstacles, we present PhotoVerse, an innovative methodology that incorporates a dual-branch conditioning mechanism in both text and image domains, providing effective control over the image generation process. Furthermore, we introduce facial identity loss as a novel component to enhance the preservation of identity during training. Remarkably, our proposed PhotoVerse eliminates the need for test time tuning and relies solely on a single facial photo of the target identity, significantly reducing the resource cost associated with image generation. After a single training phase, our approach enables generating high-quality images within only a few seconds. Moreover, our method can produce diverse images that encompass various scenes and styles. The extensive evaluation demonstrates the superior performance of our approach, which achieves the dual objectives of preserving identity and facilitating editability. Project page: https://photoverse2d.github.io/ |
|
2023-09-13T00:00:00 | 2309.06180 | Efficient Memory Management for Large Language Model Serving with PagedAttention | [
"Woosuk Kwon",
"Zhuohan Li",
"Siyuan Zhuang",
"Ying Sheng",
"Lianmin Zheng",
"Cody Hao Yu",
"Joseph E. Gonzalez",
"Hao Zhang",
"Ion Stoica"
] | https://github.com/vllm-project/vllm | High throughput serving of large language models (LLMs) requires batching sufficiently many requests at a time. However, existing systems struggle because the key-value cache (KV cache) memory for each request is huge and grows and shrinks dynamically. When managed inefficiently, this memory can be significantly wasted by fragmentation and redundant duplication, limiting the batch size. To address this problem, we propose PagedAttention, an attention algorithm inspired by the classical virtual memory and paging techniques in operating systems. On top of it, we build vLLM, an LLM serving system that achieves (1) near-zero waste in KV cache memory and (2) flexible sharing of KV cache within and across requests to further reduce memory usage. Our evaluations show that vLLM improves the throughput of popular LLMs by 2-4times with the same level of latency compared to the state-of-the-art systems, such as FasterTransformer and Orca. The improvement is more pronounced with longer sequences, larger models, and more complex decoding algorithms. vLLM's source code is publicly available at https://github.com/vllm-project/vllm |
2023-09-13T00:00:00 | 2309.05689 | Large Language Model for Science: A Study on P vs. NP | [
"Qingxiu Dong",
"Li Dong",
"Ke Xu",
"Guangyan Zhou",
"Yaru Hao",
"Zhifang Sui",
"Furu Wei"
] | In this work, we use large language models (LLMs) to augment and accelerate research on the P versus NP problem, one of the most important open problems in theoretical computer science and mathematics. Specifically, we propose Socratic reasoning, a general framework that promotes in-depth thinking with LLMs for complex problem-solving. Socratic reasoning encourages LLMs to recursively discover, solve, and integrate problems while facilitating self-evaluation and refinement. Our pilot study on the P vs. NP problem shows that GPT-4 successfully produces a proof schema and engages in rigorous reasoning throughout 97 dialogue turns, concluding "P neq NP", which is in alignment with (Xu and Zhou, 2023). The investigation uncovers novel insights within the extensive solution space of LLMs, shedding light on LLM for Science. |
|
2023-09-13T00:00:00 | 2309.06380 | InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation | [
"Xingchao Liu",
"Xiwen Zhang",
"Jianzhu Ma",
"Jian Peng",
"Qiang Liu"
] | https://github.com/gnobitab/InstaFlow | Diffusion models have revolutionized text-to-image generation with its exceptional quality and creativity. However, its multi-step sampling process is known to be slow, often requiring tens of inference steps to obtain satisfactory results. Previous attempts to improve its sampling speed and reduce computational costs through distillation have been unsuccessful in achieving a functional one-step model. In this paper, we explore a recent method called Rectified Flow, which, thus far, has only been applied to small datasets. The core of Rectified Flow lies in its reflow procedure, which straightens the trajectories of probability flows, refines the coupling between noises and images, and facilitates the distillation process with student models. We propose a novel text-conditioned pipeline to turn Stable Diffusion (SD) into an ultra-fast one-step model, in which we find reflow plays a critical role in improving the assignment between noise and images. Leveraging our new pipeline, we create, to the best of our knowledge, the first one-step diffusion-based text-to-image generator with SD-level image quality, achieving an FID (Frechet Inception Distance) of 23.3 on MS COCO 2017-5k, surpassing the previous state-of-the-art technique, progressive distillation, by a significant margin (37.2 rightarrow 23.3 in FID). By utilizing an expanded network with 1.7B parameters, we further improve the FID to 22.4. We call our one-step models InstaFlow. On MS COCO 2014-30k, InstaFlow yields an FID of 13.1 in just 0.09 second, the best in leq 0.1 second regime, outperforming the recent StyleGAN-T (13.9 in 0.1 second). Notably, the training of InstaFlow only costs 199 A100 GPU days. Project page:~https://github.com/gnobitab/InstaFlow. |
2023-09-13T00:00:00 | 2309.06126 | AstroLLaMA: Towards Specialized Foundation Models in Astronomy | [
"Tuan Dung Nguyen",
"Yuan-Sen Ting",
"Ioana Ciucă",
"Charlie O'Neill",
"Ze-Chang Sun",
"Maja Jabłońska",
"Sandor Kruk",
"Ernest Perkowski",
"Jack Miller",
"Jason Li",
"Josh Peek",
"Kartheik Iyer",
"Tomasz Różański",
"Pranav Khetarpal",
"Sharaf Zaman",
"David Brodrick",
"Sergio J. Rodríguez Méndez",
"Thang Bui",
"Alyssa Goodman",
"Alberto Accomazzi",
"Jill Naiman",
"Jesse Cranney",
"Kevin Schawinski",
"UniverseTBD"
] | Large language models excel in many human-language tasks but often falter in highly specialized domains like scholarly astronomy. To bridge this gap, we introduce AstroLLaMA, a 7-billion-parameter model fine-tuned from LLaMA-2 using over 300,000 astronomy abstracts from arXiv. Optimized for traditional causal language modeling, AstroLLaMA achieves a 30% lower perplexity than Llama-2, showing marked domain adaptation. Our model generates more insightful and scientifically relevant text completions and embedding extraction than state-of-the-arts foundation models despite having significantly fewer parameters. AstroLLaMA serves as a robust, domain-specific model with broad fine-tuning potential. Its public release aims to spur astronomy-focused research, including automatic paper summarization and conversational agent development. |
|
2023-09-13T00:00:00 | 2309.05858 | Uncovering mesa-optimization algorithms in Transformers | [
"Johannes von Oswald",
"Eyvind Niklasson",
"Maximilian Schlegel",
"Seijin Kobayashi",
"Nicolas Zucchet",
"Nino Scherrer",
"Nolan Miller",
"Mark Sandler",
"Blaise Agüera y Arcas",
"Max Vladymyrov",
"Razvan Pascanu",
"João Sacramento"
] | Transformers have become the dominant model in deep learning, but the reason for their superior performance is poorly understood. Here, we hypothesize that the strong performance of Transformers stems from an architectural bias towards mesa-optimization, a learned process running within the forward pass of a model consisting of the following two steps: (i) the construction of an internal learning objective, and (ii) its corresponding solution found through optimization. To test this hypothesis, we reverse-engineer a series of autoregressive Transformers trained on simple sequence modeling tasks, uncovering underlying gradient-based mesa-optimization algorithms driving the generation of predictions. Moreover, we show that the learned forward-pass optimization algorithm can be immediately repurposed to solve supervised few-shot tasks, suggesting that mesa-optimization might underlie the in-context learning capabilities of large language models. Finally, we propose a novel self-attention layer, the mesa-layer, that explicitly and efficiently solves optimization problems specified in context. We find that this layer can lead to improved performance in synthetic and preliminary language modeling experiments, adding weight to our hypothesis that mesa-optimization is an important operation hidden within the weights of trained Transformers. |
|
2023-09-13T00:00:00 | 2309.05767 | Natural Language Supervision for General-Purpose Audio Representations | [
"Benjamin Elizalde",
"Soham Deshmukh",
"Huaming Wang"
] | Audio-Language models jointly learn multimodal text and audio representations that enable Zero-Shot inference. Models rely on the encoders to create powerful representations of the input and generalize to multiple tasks ranging from sounds, music, and speech. Although models have achieved remarkable performance, there is still a performance gap with task-specific models. In this paper, we propose a Contrastive Language-Audio Pretraining model that is pretrained with a diverse collection of 4.6M audio-text pairs employing two innovative encoders for Zero-Shot inference. To learn audio representations, we trained an audio encoder on 22 audio tasks, instead of the standard training of sound event classification. To learn language representations, we trained an autoregressive decoder-only model instead of the standard encoder-only models. Then, the audio and language representations are brought into a joint multimodal space using Contrastive Learning. We used our encoders to improve the downstream performance by a margin. We extensively evaluated the generalization of our representations on 26 downstream tasks, the largest in the literature. Our model achieves state of the art results in several tasks leading the way towards general-purpose audio representations. |
|
2023-09-13T00:00:00 | 2309.06441 | Learning Disentangled Avatars with Hybrid 3D Representations | [
"Yao Feng",
"Weiyang Liu",
"Timo Bolkart",
"Jinlong Yang",
"Marc Pollefeys",
"Michael J. Black"
] | Tremendous efforts have been made to learn animatable and photorealistic human avatars. Towards this end, both explicit and implicit 3D representations are heavily studied for a holistic modeling and capture of the whole human (e.g., body, clothing, face and hair), but neither representation is an optimal choice in terms of representation efficacy since different parts of the human avatar have different modeling desiderata. For example, meshes are generally not suitable for modeling clothing and hair. Motivated by this, we present Disentangled Avatars~(DELTA), which models humans with hybrid explicit-implicit 3D representations. DELTA takes a monocular RGB video as input, and produces a human avatar with separate body and clothing/hair layers. Specifically, we demonstrate two important applications for DELTA. For the first one, we consider the disentanglement of the human body and clothing and in the second, we disentangle the face and hair. To do so, DELTA represents the body or face with an explicit mesh-based parametric 3D model and the clothing or hair with an implicit neural radiance field. To make this possible, we design an end-to-end differentiable renderer that integrates meshes into volumetric rendering, enabling DELTA to learn directly from monocular videos without any 3D supervision. Finally, we show that how these two applications can be easily combined to model full-body avatars, such that the hair, face, body and clothing can be fully disentangled yet jointly rendered. Such a disentanglement enables hair and clothing transfer to arbitrary body shapes. We empirically validate the effectiveness of DELTA's disentanglement by demonstrating its promising performance on disentangled reconstruction, virtual clothing try-on and hairstyle transfer. To facilitate future research, we also release an open-sourced pipeline for the study of hybrid human avatar modeling. |
|
2023-09-13T00:00:00 | 2309.06440 | LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot Learning | [
"Kenneth Shaw",
"Ananye Agarwal",
"Deepak Pathak"
] | Dexterous manipulation has been a long-standing challenge in robotics. While machine learning techniques have shown some promise, results have largely been currently limited to simulation. This can be mostly attributed to the lack of suitable hardware. In this paper, we present LEAP Hand, a low-cost dexterous and anthropomorphic hand for machine learning research. In contrast to previous hands, LEAP Hand has a novel kinematic structure that allows maximal dexterity regardless of finger pose. LEAP Hand is low-cost and can be assembled in 4 hours at a cost of 2000 USD from readily available parts. It is capable of consistently exerting large torques over long durations of time. We show that LEAP Hand can be used to perform several manipulation tasks in the real world -- from visual teleoperation to learning from passive video data and sim2real. LEAP Hand significantly outperforms its closest competitor Allegro Hand in all our experiments while being 1/8th of the cost. We release detailed assembly instructions, the Sim2Real pipeline and a development platform with useful APIs on our website at https://leap-hand.github.io/ |
|
2023-09-14T00:00:00 | 2309.06657 | Statistical Rejection Sampling Improves Preference Optimization | [
"Tianqi Liu",
"Yao Zhao",
"Rishabh Joshi",
"Misha Khalman",
"Mohammad Saleh",
"Peter J. Liu",
"Jialu Liu"
] | Improving the alignment of language models with human preferences remains an active research challenge. Previous approaches have primarily utilized Reinforcement Learning from Human Feedback (RLHF) via online RL methods such as Proximal Policy Optimization (PPO). Recently, offline methods such as Sequence Likelihood Calibration (SLiC) and Direct Preference Optimization (DPO) have emerged as attractive alternatives, offering improvements in stability and scalability while maintaining competitive performance. SLiC refines its loss function using sequence pairs sampled from a supervised fine-tuned (SFT) policy, while DPO directly optimizes language models based on preference data, foregoing the need for a separate reward model. However, the maximum likelihood estimator (MLE) of the target optimal policy requires labeled preference pairs sampled from that policy. DPO's lack of a reward model constrains its ability to sample preference pairs from the optimal policy, and SLiC is restricted to sampling preference pairs only from the SFT policy. To address these limitations, we introduce a novel approach called Statistical Rejection Sampling Optimization (RSO) that aims to source preference data from the target optimal policy using rejection sampling, enabling a more accurate estimation of the optimal policy. We also propose a unified framework that enhances the loss functions used in both SLiC and DPO from a preference modeling standpoint. Through extensive experiments across three diverse tasks, we demonstrate that RSO consistently outperforms both SLiC and DPO on evaluations from both Large Language Model (LLM) and human raters. |
|
2023-09-14T00:00:00 | 2309.07062 | Large Language Models for Compiler Optimization | [
"Chris Cummins",
"Volker Seeker",
"Dejan Grubisic",
"Mostafa Elhoushi",
"Youwei Liang",
"Baptiste Roziere",
"Jonas Gehring",
"Fabian Gloeckle",
"Kim Hazelwood",
"Gabriel Synnaeve",
"Hugh Leather"
] | We explore the novel application of Large Language Models to code optimization. We present a 7B-parameter transformer model trained from scratch to optimize LLVM assembly for code size. The model takes as input unoptimized assembly and outputs a list of compiler options to best optimize the program. Crucially, during training, we ask the model to predict the instruction counts before and after optimization, and the optimized code itself. These auxiliary learning tasks significantly improve the optimization performance of the model and improve the model's depth of understanding. We evaluate on a large suite of test programs. Our approach achieves a 3.0% improvement in reducing instruction counts over the compiler, outperforming two state-of-the-art baselines that require thousands of compilations. Furthermore, the model shows surprisingly strong code reasoning abilities, generating compilable code 91% of the time and perfectly emulating the output of the compiler 70% of the time. |
|
2023-09-14T00:00:00 | 2309.06895 | MagiCapture: High-Resolution Multi-Concept Portrait Customization | [
"Junha Hyung",
"Jaeyo Shin",
"Jaegul Choo"
] | Large-scale text-to-image models including Stable Diffusion are capable of generating high-fidelity photorealistic portrait images. There is an active research area dedicated to personalizing these models, aiming to synthesize specific subjects or styles using provided sets of reference images. However, despite the plausible results from these personalization methods, they tend to produce images that often fall short of realism and are not yet on a commercially viable level. This is particularly noticeable in portrait image generation, where any unnatural artifact in human faces is easily discernible due to our inherent human bias. To address this, we introduce MagiCapture, a personalization method for integrating subject and style concepts to generate high-resolution portrait images using just a few subject and style references. For instance, given a handful of random selfies, our fine-tuned model can generate high-quality portrait images in specific styles, such as passport or profile photos. The main challenge with this task is the absence of ground truth for the composed concepts, leading to a reduction in the quality of the final output and an identity shift of the source subject. To address these issues, we present a novel Attention Refocusing loss coupled with auxiliary priors, both of which facilitate robust learning within this weakly supervised learning setting. Our pipeline also includes additional post-processing steps to ensure the creation of highly realistic outputs. MagiCapture outperforms other baselines in both quantitative and qualitative evaluations and can also be generalized to other non-human objects. |
|
2023-09-14T00:00:00 | 2309.07125 | Text-Guided Generation and Editing of Compositional 3D Avatars | [
"Hao Zhang",
"Yao Feng",
"Peter Kulits",
"Yandong Wen",
"Justus Thies",
"Michael J. Black"
] | Our goal is to create a realistic 3D facial avatar with hair and accessories using only a text description. While this challenge has attracted significant recent interest, existing methods either lack realism, produce unrealistic shapes, or do not support editing, such as modifications to the hairstyle. We argue that existing methods are limited because they employ a monolithic modeling approach, using a single representation for the head, face, hair, and accessories. Our observation is that the hair and face, for example, have very different structural qualities that benefit from different representations. Building on this insight, we generate avatars with a compositional model, in which the head, face, and upper body are represented with traditional 3D meshes, and the hair, clothing, and accessories with neural radiance fields (NeRF). The model-based mesh representation provides a strong geometric prior for the face region, improving realism while enabling editing of the person's appearance. By using NeRFs to represent the remaining components, our method is able to model and synthesize parts with complex geometry and appearance, such as curly hair and fluffy scarves. Our novel system synthesizes these high-quality compositional avatars from text descriptions. The experimental results demonstrate that our method, Text-guided generation and Editing of Compositional Avatars (TECA), produces avatars that are more realistic than those of recent methods while being editable because of their compositional nature. For example, our TECA enables the seamless transfer of compositional features like hairstyles, scarves, and other accessories between avatars. This capability supports applications such as virtual try-on. |
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2023-09-14T00:00:00 | 2309.06802 | Dynamic NeRFs for Soccer Scenes | [
"Sacha Lewin",
"Maxime Vandegar",
"Thomas Hoyoux",
"Olivier Barnich",
"Gilles Louppe"
] | The long-standing problem of novel view synthesis has many applications, notably in sports broadcasting. Photorealistic novel view synthesis of soccer actions, in particular, is of enormous interest to the broadcast industry. Yet only a few industrial solutions have been proposed, and even fewer that achieve near-broadcast quality of the synthetic replays. Except for their setup of multiple static cameras around the playfield, the best proprietary systems disclose close to no information about their inner workings. Leveraging multiple static cameras for such a task indeed presents a challenge rarely tackled in the literature, for a lack of public datasets: the reconstruction of a large-scale, mostly static environment, with small, fast-moving elements. Recently, the emergence of neural radiance fields has induced stunning progress in many novel view synthesis applications, leveraging deep learning principles to produce photorealistic results in the most challenging settings. In this work, we investigate the feasibility of basing a solution to the task on dynamic NeRFs, i.e., neural models purposed to reconstruct general dynamic content. We compose synthetic soccer environments and conduct multiple experiments using them, identifying key components that help reconstruct soccer scenes with dynamic NeRFs. We show that, although this approach cannot fully meet the quality requirements for the target application, it suggests promising avenues toward a cost-efficient, automatic solution. We also make our work dataset and code publicly available, with the goal to encourage further efforts from the research community on the task of novel view synthesis for dynamic soccer scenes. For code, data, and video results, please see https://soccernerfs.isach.be. |
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2023-09-14T00:00:00 | 2309.06933 | DreamStyler: Paint by Style Inversion with Text-to-Image Diffusion Models | [
"Namhyuk Ahn",
"Junsoo Lee",
"Chunggi Lee",
"Kunhee Kim",
"Daesik Kim",
"Seung-Hun Nam",
"Kibeom Hong"
] | Recent progresses in large-scale text-to-image models have yielded remarkable accomplishments, finding various applications in art domain. However, expressing unique characteristics of an artwork (e.g. brushwork, colortone, or composition) with text prompts alone may encounter limitations due to the inherent constraints of verbal description. To this end, we introduce DreamStyler, a novel framework designed for artistic image synthesis, proficient in both text-to-image synthesis and style transfer. DreamStyler optimizes a multi-stage textual embedding with a context-aware text prompt, resulting in prominent image quality. In addition, with content and style guidance, DreamStyler exhibits flexibility to accommodate a range of style references. Experimental results demonstrate its superior performance across multiple scenarios, suggesting its promising potential in artistic product creation. |
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2023-09-14T00:00:00 | 2309.07122 | Tree-Structured Shading Decomposition | [
"Chen Geng",
"Hong-Xing Yu",
"Sharon Zhang",
"Maneesh Agrawala",
"Jiajun Wu"
] | We study inferring a tree-structured representation from a single image for object shading. Prior work typically uses the parametric or measured representation to model shading, which is neither interpretable nor easily editable. We propose using the shade tree representation, which combines basic shading nodes and compositing methods to factorize object surface shading. The shade tree representation enables novice users who are unfamiliar with the physical shading process to edit object shading in an efficient and intuitive manner. A main challenge in inferring the shade tree is that the inference problem involves both the discrete tree structure and the continuous parameters of the tree nodes. We propose a hybrid approach to address this issue. We introduce an auto-regressive inference model to generate a rough estimation of the tree structure and node parameters, and then we fine-tune the inferred shade tree through an optimization algorithm. We show experiments on synthetic images, captured reflectance, real images, and non-realistic vector drawings, allowing downstream applications such as material editing, vectorized shading, and relighting. Project website: https://chen-geng.com/inv-shade-trees |
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2023-09-15T00:00:00 | 2309.07430 | Clinical Text Summarization: Adapting Large Language Models Can Outperform Human Experts | [
"Dave Van Veen",
"Cara Van Uden",
"Louis Blankemeier",
"Jean-Benoit Delbrouck",
"Asad Aali",
"Christian Bluethgen",
"Anuj Pareek",
"Malgorzata Polacin",
"William Collins",
"Neera Ahuja",
"Curtis P. Langlotz",
"Jason Hom",
"Sergios Gatidis",
"John Pauly",
"Akshay S. Chaudhari"
] | Sifting through vast textual data and summarizing key information imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown immense promise in natural language processing (NLP) tasks, their efficacy across diverse clinical summarization tasks has not yet been rigorously examined. In this work, we employ domain adaptation methods on eight LLMs, spanning six datasets and four distinct summarization tasks: radiology reports, patient questions, progress notes, and doctor-patient dialogue. Our thorough quantitative assessment reveals trade-offs between models and adaptation methods in addition to instances where recent advances in LLMs may not lead to improved results. Further, in a clinical reader study with six physicians, we depict that summaries from the best adapted LLM are preferable to human summaries in terms of completeness and correctness. Our ensuing qualitative analysis delineates mutual challenges faced by both LLMs and human experts. Lastly, we correlate traditional quantitative NLP metrics with reader study scores to enhance our understanding of how these metrics align with physician preferences. Our research marks the first evidence of LLMs outperforming human experts in clinical text summarization across multiple tasks. This implies that integrating LLMs into clinical workflows could alleviate documentation burden, empowering clinicians to focus more on personalized patient care and other irreplaceable human aspects of medicine. |
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2023-09-15T00:00:00 | 2309.07900 | Ambiguity-Aware In-Context Learning with Large Language Models | [
"Lingyu Gao",
"Aditi Chaudhary",
"Krishna Srinivasan",
"Kazuma Hashimoto",
"Karthik Raman",
"Michael Bendersky"
] | In-context learning (ICL) i.e. showing LLMs only a few task-specific demonstrations has led to downstream gains with no task-specific fine-tuning required. However, LLMs are sensitive to the choice of prompts, and therefore a crucial research question is how to select good demonstrations for ICL. One effective strategy is leveraging semantic similarity between the ICL demonstrations and test inputs by using a text retriever, which however is sub-optimal as that does not consider the LLM's existing knowledge about that task. From prior work (Min et al., 2022), we already know that labels paired with the demonstrations bias the model predictions. This leads us to our hypothesis whether considering LLM's existing knowledge about the task, especially with respect to the output label space can help in a better demonstration selection strategy. Through extensive experimentation on three text classification tasks, we find that it is beneficial to not only choose semantically similar ICL demonstrations but also to choose those demonstrations that help resolve the inherent label ambiguity surrounding the test example. Interestingly, we find that including demonstrations that the LLM previously mis-classified and also fall on the test example's decision boundary, brings the most performance gain. |
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2023-09-15T00:00:00 | 2309.07870 | Agents: An Open-source Framework for Autonomous Language Agents | [
"Wangchunshu Zhou",
"Yuchen Eleanor Jiang",
"Long Li",
"Jialong Wu",
"Tiannan Wang",
"Shi Qiu",
"Jintian Zhang",
"Jing Chen",
"Ruipu Wu",
"Shuai Wang",
"Shiding Zhu",
"Jiyu Chen",
"Wentao Zhang",
"Ningyu Zhang",
"Huajun Chen",
"Peng Cui",
"Mrinmaya Sachan"
] | https://github.com/aiwaves-cn/agents | Recent advances on large language models (LLMs) enable researchers and developers to build autonomous language agents that can automatically solve various tasks and interact with environments, humans, and other agents using natural language interfaces. We consider language agents as a promising direction towards artificial general intelligence and release Agents, an open-source library with the goal of opening up these advances to a wider non-specialist audience. Agents is carefully engineered to support important features including planning, memory, tool usage, multi-agent communication, and fine-grained symbolic control. Agents is user-friendly as it enables non-specialists to build, customize, test, tune, and deploy state-of-the-art autonomous language agents without much coding. The library is also research-friendly as its modularized design makes it easily extensible for researchers. Agents is available at https://github.com/aiwaves-cn/agents. |
2023-09-15T00:00:00 | 2309.07462 | Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation? | [
"Rishav Hada",
"Varun Gumma",
"Adrian de Wynter",
"Harshita Diddee",
"Mohamed Ahmed",
"Monojit Choudhury",
"Kalika Bali",
"Sunayana Sitaram"
] | Large Language Models (LLMs) have demonstrated impressive performance on Natural Language Processing (NLP) tasks, such as Question Answering, Summarization, and Classification. The use of LLMs as evaluators, that can rank or score the output of other models (usually LLMs) has become increasingly popular, due to the limitations of current evaluation techniques including the lack of appropriate benchmarks, metrics, cost, and access to human annotators. While LLMs are capable of handling approximately 100 languages, the majority of languages beyond the top 20 lack systematic evaluation across various tasks, metrics, and benchmarks. This creates an urgent need to scale up multilingual evaluation to ensure a precise understanding of LLM performance across diverse languages. LLM-based evaluators seem like the perfect solution to this problem, as they do not require human annotators, human-created references, or benchmarks and can theoretically be used to evaluate any language covered by the LLM. In this paper, we investigate whether LLM-based evaluators can help scale up multilingual evaluation. Specifically, we calibrate LLM-based evaluation against 20k human judgments of five metrics across three text-generation tasks in eight languages. Our findings indicate that LLM-based evaluators may exhibit bias towards higher scores and should be used with caution and should always be calibrated with a dataset of native speaker judgments, particularly in low-resource and non-Latin script languages. |
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2023-09-15T00:00:00 | 2309.07749 | OmnimatteRF: Robust Omnimatte with 3D Background Modeling | [
"Geng Lin",
"Chen Gao",
"Jia-Bin Huang",
"Changil Kim",
"Yipeng Wang",
"Matthias Zwicker",
"Ayush Saraf"
] | Video matting has broad applications, from adding interesting effects to casually captured movies to assisting video production professionals. Matting with associated effects such as shadows and reflections has also attracted increasing research activity, and methods like Omnimatte have been proposed to separate dynamic foreground objects of interest into their own layers. However, prior works represent video backgrounds as 2D image layers, limiting their capacity to express more complicated scenes, thus hindering application to real-world videos. In this paper, we propose a novel video matting method, OmnimatteRF, that combines dynamic 2D foreground layers and a 3D background model. The 2D layers preserve the details of the subjects, while the 3D background robustly reconstructs scenes in real-world videos. Extensive experiments demonstrate that our method reconstructs scenes with better quality on various videos. |
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2023-09-15T00:00:00 | 2309.07906 | Generative Image Dynamics | [
"Zhengqi Li",
"Richard Tucker",
"Noah Snavely",
"Aleksander Holynski"
] | We present an approach to modeling an image-space prior on scene dynamics. Our prior is learned from a collection of motion trajectories extracted from real video sequences containing natural, oscillating motion such as trees, flowers, candles, and clothes blowing in the wind. Given a single image, our trained model uses a frequency-coordinated diffusion sampling process to predict a per-pixel long-term motion representation in the Fourier domain, which we call a neural stochastic motion texture. This representation can be converted into dense motion trajectories that span an entire video. Along with an image-based rendering module, these trajectories can be used for a number of downstream applications, such as turning still images into seamlessly looping dynamic videos, or allowing users to realistically interact with objects in real pictures. |
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2023-09-15T00:00:00 | 2309.07314 | AudioSR: Versatile Audio Super-resolution at Scale | [
"Haohe Liu",
"Ke Chen",
"Qiao Tian",
"Wenwu Wang",
"Mark D. Plumbley"
] | https://github.com/haoheliu/versatile_audio_super_resolution | Audio super-resolution is a fundamental task that predicts high-frequency components for low-resolution audio, enhancing audio quality in digital applications. Previous methods have limitations such as the limited scope of audio types (e.g., music, speech) and specific bandwidth settings they can handle (e.g., 4kHz to 8kHz). In this paper, we introduce a diffusion-based generative model, AudioSR, that is capable of performing robust audio super-resolution on versatile audio types, including sound effects, music, and speech. Specifically, AudioSR can upsample any input audio signal within the bandwidth range of 2kHz to 16kHz to a high-resolution audio signal at 24kHz bandwidth with a sampling rate of 48kHz. Extensive objective evaluation on various audio super-resolution benchmarks demonstrates the strong result achieved by the proposed model. In addition, our subjective evaluation shows that AudioSR can acts as a plug-and-play module to enhance the generation quality of a wide range of audio generative models, including AudioLDM, Fastspeech2, and MusicGen. Our code and demo are available at https://audioldm.github.io/audiosr. |
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