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We propose RUDDER, a novel reinforcement learning approach for delayed rewards in finite Markov decision processes (MDPs). In MDPs the Q-values are equal to the expected immediate reward plus the expected future rewards. The latter are related to bias problems in temporal difference (TD) learning and to high variance problems in Monte Carlo (MC) learning. Both problems are even more severe when rewards are delayed. RUDDER aims at making the expected future rewards zero, which simplifies Q-value estimation to computing the mean of the immediate reward. We propose the following two new concepts to push the expected future rewards toward zero. (i) Reward redistribution that leads to return-equivalent decision processes with the same optimal policies and, when optimal, zero expected future rewards. (ii) Return decomposition via contribution analysis which transforms the reinforcement learning task into a regression task at which deep learning excels. On artificial tasks with delayed rewards, RUDDER is significantly faster than MC and exponentially faster than Monte Carlo Tree Search (MCTS), TD({\lambda}), and reward shaping approaches. At Atari games, RUDDER on top of a Proximal Policy Optimization (PPO) baseline improves the scores, which is most prominent at games with delayed rewards. Source code is available at \url{https://github.com/ml-jku/rudder} and demonstration videos at \url{https://goo.gl/EQerZV}. | [] | [
"Atari Games",
"Regression"
] | [] | [
"Atari 2600 Bowling",
"Atari 2600 Yars Revenge"
] | [
"Score"
] | RUDDER: Return Decomposition for Delayed Rewards |
Video super-resolution (SR) aims to generate a sequence of high-resolution
(HR) frames with plausible and temporally consistent details from their
low-resolution (LR) counterparts. The generation of accurate correspondence
plays a significant role in video SR. It is demonstrated by traditional video
SR methods that simultaneous SR of both images and optical flows can provide
accurate correspondences and better SR results. However, LR optical flows are
used in existing deep learning based methods for correspondence generation. In
this paper, we propose an end-to-end trainable video SR framework to
super-resolve both images and optical flows. Specifically, we first propose an
optical flow reconstruction network (OFRnet) to infer HR optical flows in a
coarse-to-fine manner. Then, motion compensation is performed according to the
HR optical flows. Finally, compensated LR inputs are fed to a super-resolution
network (SRnet) to generate the SR results. Extensive experiments demonstrate
that HR optical flows provide more accurate correspondences than their LR
counterparts and improve both accuracy and consistency performance. Comparative
results on the Vid4 and DAVIS-10 datasets show that our framework achieves the
state-of-the-art performance. | [] | [
"Motion Compensation",
"Optical Flow Estimation",
"Super-Resolution",
"Video Super-Resolution"
] | [] | [
"Vid4 - 4x upscaling"
] | [
"SSIM",
"PSNR",
"MOVIE"
] | Learning for Video Super-Resolution through HR Optical Flow Estimation |
In this paper, we address the problem of enhancing the speech of a speaker of interest in a cocktail party scenario when visual information of the speaker of interest is available. Contrary to most previous studies, we do not learn visual features on the typically small audio-visual datasets, but use an already available face landmark detector (trained on a separate image dataset). The landmarks are used by LSTM-based models to generate time-frequency masks which are applied to the acoustic mixed-speech spectrogram. Results show that: (i) landmark motion features are very effective features for this task, (ii) similarly to previous work, reconstruction of the target speaker's spectrogram mediated by masking is significantly more accurate than direct spectrogram reconstruction, and (iii) the best masks depend on both motion landmark features and the input mixed-speech spectrogram. To the best of our knowledge, our proposed models are the first models trained and evaluated on the limited size GRID and TCD-TIMIT datasets, that achieve speaker-independent speech enhancement in a multi-talker setting. | [] | [
"Speech Enhancement",
"Speech Separation"
] | [] | [
"GRID corpus (mixed-speech)",
"TCD-TIMIT corpus (mixed-speech)"
] | [
"SDR",
"PESQ"
] | Face Landmark-based Speaker-Independent Audio-Visual Speech Enhancement in Multi-Talker Environments |
Many seemingly unrelated computer vision tasks can be viewed as a special
case of image decomposition into separate layers. For example, image
segmentation (separation into foreground and background layers); transparent
layer separation (into reflection and transmission layers); Image dehazing
(separation into a clear image and a haze map), and more. In this paper we
propose a unified framework for unsupervised layer decomposition of a single
image, based on coupled "Deep-image-Prior" (DIP) networks. It was shown
[Ulyanov et al] that the structure of a single DIP generator network is
sufficient to capture the low-level statistics of a single image. We show that
coupling multiple such DIPs provides a powerful tool for decomposing images
into their basic components, for a wide variety of applications. This
capability stems from the fact that the internal statistics of a mixture of
layers is more complex than the statistics of each of its individual
components. We show the power of this approach for Image-Dehazing, Fg/Bg
Segmentation, Watermark-Removal, Transparency Separation in images and video,
and more. These capabilities are achieved in a totally unsupervised way, with
no training examples other than the input image/video itself. | [] | [
"Image Dehazing",
"Semantic Segmentation",
"Transparency Separation"
] | [] | [
"O-Haze"
] | [
"PSNR"
] | "Double-DIP": Unsupervised Image Decomposition via Coupled Deep-Image-Priors |
We demonstrate that for sentence-level relation extraction it is beneficial to consider other relations in the sentential context while predicting the target relation. Our architecture uses an LSTM-based encoder to jointly learn representations for all relations in a single sentence. We combine the context representations with an attention mechanism to make the final prediction. We use the Wikidata knowledge base to construct a dataset of multiple relations per sentence and to evaluate our approach. Compared to a baseline system, our method results in an average error reduction of 24 on a held-out set of relations. The code and the dataset to replicate the experiments are made available at \url{https://github.com/ukplab/}. | [] | [
"Question Answering",
"Relation Extraction"
] | [] | [
"Wikipedia-Wikidata relations"
] | [
"Error rate"
] | Context-Aware Representations for Knowledge Base Relation Extraction |
Previous CNN-based video super-resolution approaches need to align multiple
frames to the reference. In this paper, we show that proper frame alignment and
motion compensation is crucial for achieving high quality results. We
accordingly propose a `sub-pixel motion compensation' (SPMC) layer in a CNN
framework. Analysis and experiments show the suitability of this layer in video
SR. The final end-to-end, scalable CNN framework effectively incorporates the
SPMC layer and fuses multiple frames to reveal image details. Our
implementation can generate visually and quantitatively high-quality results,
superior to current state-of-the-arts, without the need of parameter tuning. | [] | [
"Image Super-Resolution",
"Motion Compensation",
"Super-Resolution",
"Video Super-Resolution"
] | [] | [
"Set5 - 4x upscaling",
"Vid4 - 4x upscaling",
"Set14 - 4x upscaling"
] | [
"SSIM",
"PSNR"
] | Detail-revealing Deep Video Super-resolution |
Unsupervised domain adaptation aims to transfer knowledge from a source domain to a target domain so that the target domain data can be recognized without any explicit labelling information for this domain. One limitation of the problem setting is that testing data, despite having no labels, from the target domain is needed during training, which prevents the trained model being directly applied to classify unseen test instances. We formulate a new cross-domain classification problem arising from real-world scenarios where labelled data is available for a subset of classes (known classes) in the target domain, and we expect to recognize new samples belonging to any class (known and unseen classes) once the model is learned. This is a generalized zero-shot learning problem where the side information comes from the source domain in the form of labelled samples instead of class-level semantic representations commonly used in traditional zero-shot learning. We present a unified domain adaptation framework for both unsupervised and zero-shot learning conditions. Our approach learns a joint subspace from source and target domains so that the projections of both data in the subspace can be domain invariant and easily separable. We use the supervised locality preserving projection (SLPP) as the enabling technique and conduct experiments under both unsupervised and zero-shot learning conditions, achieving state-of-the-art results on three domain adaptation benchmark datasets: Office-Caltech, Office31 and Office-Home. | [] | [
"Domain Adaptation",
"Generalized Zero-Shot Learning",
"Unsupervised Domain Adaptation",
"Zero-Shot Learning"
] | [] | [
"Office-Caltech"
] | [
"Average Accuracy"
] | Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition |
We address the problem of video representation learning without
human-annotated labels. While previous efforts address the problem by designing
novel self-supervised tasks using video data, the learned features are merely
on a frame-by-frame basis, which are not applicable to many video analytic
tasks where spatio-temporal features are prevailing. In this paper we propose a
novel self-supervised approach to learn spatio-temporal features for video
representation. Inspired by the success of two-stream approaches in video
classification, we propose to learn visual features by regressing both motion
and appearance statistics along spatial and temporal dimensions, given only the
input video data. Specifically, we extract statistical concepts (fast-motion
region and the corresponding dominant direction, spatio-temporal color
diversity, dominant color, etc.) from simple patterns in both spatial and
temporal domains. Unlike prior puzzles that are even hard for humans to solve,
the proposed approach is consistent with human inherent visual habits and
therefore easy to answer. We conduct extensive experiments with C3D to validate
the effectiveness of our proposed approach. The experiments show that our
approach can significantly improve the performance of C3D when applied to video
classification tasks. Code is available at
https://github.com/laura-wang/video_repres_mas. | [] | [
"Action Recognition",
"Representation Learning",
"Self-Supervised Action Recognition",
"Video Classification"
] | [] | [
"HMDB-51",
"HMDB51",
"UCF101"
] | [
"Average accuracy of 3 splits",
"3-fold Accuracy",
"Pre-Training Dataset",
"Top-1 Accuracy"
] | Self-supervised Spatio-temporal Representation Learning for Videos by Predicting Motion and Appearance Statistics |
The large availability of depth sensors provides valuable complementary information for salient object detection (SOD) in RGBD images. However, due to the inherent difference between RGB and depth information, extracting features from the depth channel using ImageNet pre-trained backbone models and fusing them with RGB features directly are sub-optimal. In this paper, we utilize contrast prior, which used to be a dominant cue in none deep learning based SOD approaches, into CNNs-based architecture to enhance the depth information. The enhanced depth cues are further integrated with RGB features for SOD, using a novel fluid pyramid integration, which can make better use of multi-scale cross-modal features. Comprehensive experiments on 5 challenging benchmark datasets demonstrate the superiority of the architecture CPFP over 9 state-of-the-art alternative methods.
| [] | [
"Object Detection",
"RGB-D Salient Object Detection",
"RGB Salient Object Detection",
"Salient Object Detection"
] | [] | [
"STERE",
"NLPR",
"DES",
"SIP",
"LFSD",
"NJU2K",
"SSD"
] | [
"max E-Measure",
"Average MAE",
"S-Measure",
"max F-Measure"
] | Contrast Prior and Fluid Pyramid Integration for RGBD Salient Object Detection |
Capsule networks are a recently proposed type of neural network shown to outperform alternatives in challenging shape recognition tasks. In capsule networks, scalar neurons are replaced with capsule vectors or matrices, whose entries represent different properties of objects. The relationships between objects and their parts are learned via trainable viewpoint-invariant transformation matrices, and the presence of a given object is decided by the level of agreement among votes from its parts. This interaction occurs between capsule layers and is a process called routing-by-agreement. In this paper, we propose a new capsule routing algorithm derived from Variational Bayes for fitting a mixture of transforming gaussians, and show it is possible transform our capsule network into a Capsule-VAE. Our Bayesian approach addresses some of the inherent weaknesses of MLE based models such as the variance-collapse by modelling uncertainty over capsule pose parameters. We outperform the state-of-the-art on smallNORB using 50% fewer capsules than previously reported, achieve competitive performances on CIFAR-10, Fashion-MNIST, SVHN, and demonstrate significant improvement in MNIST to affNIST generalisation over previous works. | [] | [
"Image Classification"
] | [] | [
"smallNORB"
] | [
"Classification Error"
] | Capsule Routing via Variational Bayes |
Heretofore, neural networks with external memory are restricted to single memory with lossy representations of memory interactions. A rich representation of relationships between memory pieces urges a high-order and segregated relational memory. In this paper, we propose to separate the storage of individual experiences (item memory) and their occurring relationships (relational memory). The idea is implemented through a novel Self-attentive Associative Memory (SAM) operator. Found upon outer product, SAM forms a set of associative memories that represent the hypothetical high-order relationships between arbitrary pairs of memory elements, through which a relational memory is constructed from an item memory. The two memories are wired into a single sequential model capable of both memorization and relational reasoning. We achieve competitive results with our proposed two-memory model in a diversity of machine learning tasks, from challenging synthetic problems to practical testbeds such as geometry, graph, reinforcement learning, and question answering. | [] | [
"Question Answering",
"Relational Reasoning"
] | [] | [
"bAbi"
] | [
"Mean Error Rate",
"Accuracy (trained on 10k)"
] | Self-Attentive Associative Memory |
The aim of unsupervised domain adaptation is to leverage the knowledge in a labeled (source) domain to improve a model's learning performance with an unlabeled (target) domain -- the basic strategy being to mitigate the effects of discrepancies between the two distributions. Most existing algorithms can only handle unsupervised closed set domain adaptation (UCSDA), i.e., where the source and target domains are assumed to share the same label set. In this paper, we target a more challenging but realistic setting: unsupervised open set domain adaptation (UOSDA), where the target domain has unknown classes that are not found in the source domain. This is the first study to provide a learning bound for open set domain adaptation, which we do by theoretically investigating the risk of the target classifier on unknown classes. The proposed learning bound has a special term, namely open set difference, which reflects the risk of the target classifier on unknown classes. Further, we present a novel and theoretically guided unsupervised algorithm for open set domain adaptation, called distribution alignment with ppen difference (DAOD), which is based on regularizing this open set difference bound. The experiments on several benchmark datasets show the superior performance of the proposed UOSDA method compared with the state-of-the-art methods in the literature. | [] | [
"Domain Adaptation",
"Unsupervised Domain Adaptation"
] | [] | [
"Office-31",
"Office-Home"
] | [
"Average Accuracy",
"Accuracy"
] | Open Set Domain Adaptation: Theoretical Bound and Algorithm |
Multi-view subspace clustering aims to discover the inherent structure by fusing multi-view complementary information. Most existing methods first extract multiple types of hand-crafted features and then learn a joint affinity matrix for clustering. The disadvantage lies in two aspects: 1) Multi-view relations are not embedded into feature learning. 2) The end-to-end learning manner of deep learning is not well used in multi-view clustering. To address the above issues, we propose a novel multi-view deep subspace clustering network (MvDSCN) by learning a multi-view self-representation matrix in an end-to-end manner. MvDSCN consists of two sub-networks, i.e., diversity network (Dnet) and universality network (Unet). A latent space is built upon deep convolutional auto-encoders and a self-representation matrix is learned in the latent space using a fully connected layer. Dnet learns view-specific self-representation matrices while Unet learns a common self-representation matrix for all views. To exploit the complementarity of multi-view representations, Hilbert Schmidt Independence Criterion (HSIC) is introduced as a diversity regularization, which can capture the non-linear and high-order inter-view relations. As different views share the same label space, the self-representation matrices of each view are aligned to the common one by a universality regularization. Experiments on both multi-feature and multi-modality learning validate the superiority of the proposed multi-view subspace clustering model. | [] | [
"Multi-view Subspace Clustering"
] | [] | [
"ORL"
] | [
"Accuracy"
] | Multi-view Deep Subspace Clustering Networks |
Despite the continuing efforts to improve the engagingness and consistency of chit-chat dialogue systems, the majority of current work simply focus on mimicking human-like responses, leaving understudied the aspects of modeling understanding between interlocutors. The research in cognitive science, instead, suggests that understanding is an essential signal for a high-quality chit-chat conversation. Motivated by this, we propose P^2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding. Specifically, P^2 Bot incorporates mutual persona perception to enhance the quality of personalized dialogue generation. Experiments on a large public dataset, Persona-Chat, demonstrate the effectiveness of our approach, with a considerable boost over the state-of-the-art baselines across both automatic metrics and human evaluations. | [] | [
"Dialogue Generation"
] | [] | [
"Persona-Chat"
] | [
"Avg F1"
] | You Impress Me: Dialogue Generation via Mutual Persona Perception |
Visual counting, a task that predicts the number of objects from an image/video, is an open-set problem by nature, i.e., the number of population can vary in $[0,+\infty)$ in theory. However, the collected images and labeled count values are limited in reality, which means only a small closed set is observed. Existing methods typically model this task in a regression manner, while they are likely to suffer from an unseen scene with counts out of the scope of the closed set. In fact, counting is decomposable. A dense region can always be divided until sub-region counts are within the previously observed closed set. Inspired by this idea, we propose a simple but effective approach, Spatial Divide-and- Conquer Network (S-DCNet). S-DCNet only learns from a closed set but can generalize well to open-set scenarios via S-DC. S-DCNet is also efficient. To avoid repeatedly computing sub-region convolutional features, S-DC is executed on the feature map instead of on the input image. S-DCNet achieves the state-of-the-art performance on three crowd counting datasets (ShanghaiTech, UCF_CC_50 and UCF-QNRF), a vehicle counting dataset (TRANCOS) and a plant counting dataset (MTC). Compared to the previous best methods, S-DCNet brings a 20.2% relative improvement on the ShanghaiTech Part B, 20.9% on the UCF-QNRF, 22.5% on the TRANCOS and 15.1% on the MTC. Code has been made available at: https://github. com/xhp-hust-2018-2011/S-DCNet. | [] | [
"Crowd Counting",
"Regression"
] | [] | [
"UCF CC 50",
"ShanghaiTech A",
"ShanghaiTech B"
] | [
"MAE"
] | From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer |
Large geometry (e.g., orientation) variances are the key challenges in the scene text detection. In this work, we first conduct experiments to investigate the capacity of networks for learning geometry variances on detecting scene texts, and find that networks can handle only limited text geometry variances. Then, we put forward a novel Geometry Normalization Module (GNM) with multiple branches, each of which is composed of one Scale Normalization Unit and one Orientation Normalization Unit, to normalize each text instance to one desired canonical geometry range through at least one branch. The GNM is general and readily plugged into existing convolutional neural network based text detectors to construct end-to-end Geometry Normalization Networks (GNNets). Moreover, we propose a geometry-aware training scheme to effectively train the GNNets by sampling and augmenting text instances from a uniform geometry variance distribution. Finally, experiments on popular benchmarks of ICDAR 2015 and ICDAR 2017 MLT validate that our method outperforms all the state-of-the-art approaches remarkably by obtaining one-forward test F-scores of 88.52 and 74.54 respectively. | [] | [
"Scene Text",
"Scene Text Detection"
] | [] | [
"ICDAR 2017 MLT",
"ICDAR 2015"
] | [
"F-Measure",
"Recall",
"Precision"
] | Geometry Normalization Networks for Accurate Scene Text Detection |
In this paper, we propose a novel neural single document extractive summarization model for long documents, incorporating both the global context of the whole document and the local context within the current topic. We evaluate the model on two datasets of scientific papers, Pubmed and arXiv, where it outperforms previous work, both extractive and abstractive models, on ROUGE-1, ROUGE-2 and METEOR scores. We also show that, consistently with our goal, the benefits of our method become stronger as we apply it to longer documents. Rather surprisingly, an ablation study indicates that the benefits of our model seem to come exclusively from modeling the local context, even for the longest documents. | [] | [
"Text Summarization"
] | [] | [
"arXiv",
"Pubmed"
] | [
"ROUGE-1",
"ROUGE-2"
] | Extractive Summarization of Long Documents by Combining Global and Local Context |
In self-supervised learning, a system is tasked with achieving a surrogate objective by defining alternative targets on a set of unlabeled data. The aim is to build useful representations that can be used in downstream tasks, without costly manual annotation. In this work, we propose a novel self-supervised formulation of relational reasoning that allows a learner to bootstrap a signal from information implicit in unlabeled data. Training a relation head to discriminate how entities relate to themselves (intra-reasoning) and other entities (inter-reasoning), results in rich and descriptive representations in the underlying neural network backbone, which can be used in downstream tasks such as classification and image retrieval. We evaluate the proposed method following a rigorous experimental procedure, using standard datasets, protocols, and backbones. Self-supervised relational reasoning outperforms the best competitor in all conditions by an average 14% in accuracy, and the most recent state-of-the-art model by 3%. We link the effectiveness of the method to the maximization of a Bernoulli log-likelihood, which can be considered as a proxy for maximizing the mutual information, resulting in a more efficient objective with respect to the commonly used contrastive losses. | [] | [
"Image Retrieval",
"Relational Reasoning",
"Representation Learning",
"Self-Supervised Learning"
] | [] | [
"STL-10"
] | [
"Accuracy (%)"
] | Self-Supervised Relational Reasoning for Representation Learning |
Natural language processing covers a wide variety of tasks predicting syntax, semantics, and information content, and usually each type of output is generated with specially designed architectures. In this paper, we provide the simple insight that a great variety of tasks can be represented in a single unified format consisting of labeling spans and relations between spans, thus a single task-independent model can be used across different tasks. We perform extensive experiments to test this insight on 10 disparate tasks spanning dependency parsing (syntax), semantic role labeling (semantics), relation extraction (information content), aspect based sentiment analysis (sentiment), and many others, achieving performance comparable to state-of-the-art specialized models. We further demonstrate benefits of multi-task learning, and also show that the proposed method makes it easy to analyze differences and similarities in how the model handles different tasks. Finally, we convert these datasets into a unified format to build a benchmark, which provides a holistic testbed for evaluating future models for generalized natural language analysis. | [] | [
"Aspect-Based Sentiment Analysis",
"Constituency Parsing",
"Dependency Parsing",
"Multi-Task Learning",
"Named Entity Recognition",
"Part-Of-Speech Tagging",
"Relation Extraction",
"Semantic Role Labeling",
"Semantic Role Labeling (predicted predicates)",
"Sentiment Analysis"
] | [] | [
"CoNLL 2012",
"Penn Treebank",
"WLPC",
"SemEval-2010 Task 8",
"CoNLL 2003 (English)"
] | [
"LAS",
"F1",
"F1 score",
"Accuracy"
] | Generalizing Natural Language Analysis through Span-relation Representations |
Existing domain adaptation methods aim at learning features that can be generalized among domains. These methods commonly require to update source classifier to adapt to the target domain and do not properly handle the trade off between the source domain and the target domain. In this work, instead of training a classifier to adapt to the target domain, we use a separable component called data calibrator to help the fixed source classifier recover discrimination power in the target domain, while preserving the source domain's performance. When the difference between two domains is small, the source classifier's representation is sufficient to perform well in the target domain and outperforms GAN-based methods in digits. Otherwise, the proposed method can leverage synthetic images generated by GANs to boost performance and achieve state-of-the-art performance in digits datasets and driving scene semantic segmentation. Our method empirically reveals that certain intriguing hints, which can be mitigated by adversarial attack to domain discriminators, are one of the sources for performance degradation under the domain shift. | [] | [
"Adversarial Attack",
"Domain Adaptation",
"Semantic Segmentation",
"Unsupervised Domain Adaptation"
] | [] | [
"SVHN-to-MNIST",
"GTAV-to-Cityscapes Labels",
"MNIST-to-USPS",
"USPS-to-MNIST"
] | [
"mIoU",
"Accuracy"
] | Light-weight Calibrator: a Separable Component for Unsupervised Domain Adaptation |
Rain streaks in the air appear in various blurring degrees and resolutions due to different distances from their positions to the camera. Similar rain patterns are visible in a rain image as well as its multi-scale (or multi-resolution) versions, which makes it possible to exploit such complementary information for rain streak representation. In this work, we explore the multi-scale collaborative representation for rain streaks from the perspective of input image scales and hierarchical deep features in a unified framework, termed multi-scale progressive fusion network (MSPFN) for single image rain streak removal. For similar rain streaks at different positions, we employ recurrent calculation to capture the global texture, thus allowing to explore the complementary and redundant information at the spatial dimension to characterize target rain streaks. Besides, we construct multi-scale pyramid structure, and further introduce the attention mechanism to guide the fine fusion of this correlated information from different scales. This multi-scale progressive fusion strategy not only promotes the cooperative representation, but also boosts the end-to-end training. Our proposed method is extensively evaluated on several benchmark datasets and achieves state-of-the-art results. Moreover, we conduct experiments on joint deraining, detection, and segmentation tasks, and inspire a new research direction of vision task-driven image deraining. The source code is available at \url{https://github.com/kuihua/MSPFN}. | [] | [
"Rain Removal",
"Single Image Deraining"
] | [] | [
"Test2800",
"Rain100H",
"Test100",
"Test1200",
"Rain100L"
] | [
"SSIM",
"PSNR"
] | Multi-Scale Progressive Fusion Network for Single Image Deraining |
Malware detection and classification is a challenging problem and an active area of research. Traditional machine learning methods depend almost entirely on the ability to extract a set of discriminative features into which characterize malware. However, this feature engineering process is very time consuming. On the contrary, deep learning methods replace manual feature engineering by a system that performs both feature extraction and classification from raw data at once. Despite that, a major shortfall of these methods is their inhability to consider multiple disparate sources of information when performing classification, leading them to perform poorly when compared to multimodal approaches. In this work, we introduce Orthrus, a new bimodal approach to categorize malware into families based on deep learning. Orthrus combines two modalities of data: (1) the byte sequence representing the malware’s binary content, and (2) the assembly language instructions extracted from the assembly language source code of malware, and performs automatic feature learning and classification with a convolutional neural network. The idea is to benefit from multiple feature types to reflect malware’s characteristics. The experiments carried on the Microsoft Malware Classification Challenge dataset show that our proposed solution achieves higher classification performance than deep learning approaches in the literature and n-gram based methods. | [] | [
"Feature Engineering",
"Malware Classification",
"Malware Detection"
] | [] | [
"Microsoft Malware Classification Challenge"
] | [
"Accuracy (10-fold)",
"Macro F1 (10-fold)"
] | Orthrus: A Bimodal Learning Architecture for Malware Classification |
Colonoscopy is an effective technique for detecting colorectal polyps, which are highly related to colorectal cancer. In clinical practice, segmenting polyps from colonoscopy images is of great importance since it provides valuable information for diagnosis and surgery. However, accurate polyp segmentation is a challenging task, for two major reasons: (i) the same type of polyps has a diversity of size, color and texture; and (ii) the boundary between a polyp and its surrounding mucosa is not sharp. To address these challenges, we propose a parallel reverse attention network (PraNet) for accurate polyp segmentation in colonoscopy images. Specifically, we first aggregate the features in high-level layers using a parallel partial decoder (PPD). Based on the combined feature, we then generate a global map as the initial guidance area for the following components. In addition, we mine the boundary cues using a reverse attention (RA) module, which is able to establish the relationship between areas and boundary cues. Thanks to the recurrent cooperation mechanism between areas and boundaries, our PraNet is capable of calibrating any misaligned predictions, improving the segmentation accuracy. Quantitative and qualitative evaluations on five challenging datasets across six metrics show that our PraNet improves the segmentation accuracy significantly, and presents a number of advantages in terms of generalizability, and real-time segmentation efficiency. | [] | [
"Camouflaged Object Segmentation",
"Camouflage Segmentation",
"Medical Image Segmentation"
] | [] | [
"ETIS-LARIBPOLYPDB",
"Kvasir-SEG",
"CAMO",
"CVC-ClinicDB"
] | [
"max E-Measure",
"S-Measure",
"mean Dice",
"Weighted F-Measure",
"Average MAE",
"mIoU",
"MAE",
"DSC",
"E-Measure"
] | PraNet: Parallel Reverse Attention Network for Polyp Segmentation |
The main purpose of RGB-D salient object detection (SOD) is how to better integrate and utilize cross-modal fusion information. In this paper, we explore these issues from a new perspective. We integrate the features of different modalities through densely connected structures and use their mixed features to generate dynamic filters with receptive fields of different sizes. In the end, we implement a kind of more flexible and efficient multi-scale cross-modal feature processing, i.e. dynamic dilated pyramid module. In order to make the predictions have sharper edges and consistent saliency regions, we design a hybrid enhanced loss function to further optimize the results. This loss function is also validated to be effective in the single-modal RGB SOD task. In terms of six metrics, the proposed method outperforms the existing twelve methods on eight challenging benchmark datasets. A large number of experiments verify the effectiveness of the proposed module and loss function. Our code, model and results are available at \url{https://github.com/lartpang/HDFNet}. | [] | [
"Object Detection",
"RGB-D Salient Object Detection",
"RGB Salient Object Detection",
"Salient Object Detection"
] | [] | [
"NJU2K"
] | [
"Average MAE",
"S-Measure"
] | Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection |
Extracting accurate foreground animals from natural animal images benefits many downstream applications such as film production and augmented reality. However, the various appearance and furry characteristics of animals challenge existing matting methods, which usually require extra user inputs such as trimap or scribbles. To resolve these problems, we study the distinct roles of semantics and details for image matting and decompose the task into two parallel sub-tasks: high-level semantic segmentation and low-level details matting. Specifically, we propose a novel Glance and Focus Matting network (GFM), which employs a shared encoder and two separate decoders to learn both tasks in a collaborative manner for end-to-end animal image matting. Besides, we establish a novel Animal Matting dataset (AM-2k) containing 2,000 high-resolution natural animal images from 20 categories along with manually labeled alpha mattes. Furthermore, we investigate the domain gap issue between composite images and natural images systematically by conducting comprehensive analyses of various discrepancies between foreground and background images. We find that a carefully designed composition route RSSN that aims to reduce the discrepancies can lead to a better model with remarkable generalization ability. Comprehensive empirical studies on AM-2k demonstrate that GFM outperforms state-of-the-art methods and effectively reduces the generalization error. | [] | [
"Image Matting",
"Semantic Segmentation"
] | [] | [
"AM-2K"
] | [
"MSE",
"MAD",
"SAD"
] | End-to-end Animal Image Matting |
We propose a simple data augmentation technique that can be applied to standard model-free reinforcement learning algorithms, enabling robust learning directly from pixels without the need for auxiliary losses or pre-training. The approach leverages input perturbations commonly used in computer vision tasks to regularize the value function. Existing model-free approaches, such as Soft Actor-Critic (SAC), are not able to train deep networks effectively from image pixels. However, the addition of our augmentation method dramatically improves SAC's performance, enabling it to reach state-of-the-art performance on the DeepMind control suite, surpassing model-based (Dreamer, PlaNet, and SLAC) methods and recently proposed contrastive learning (CURL). Our approach can be combined with any model-free reinforcement learning algorithm, requiring only minor modifications. An implementation can be found at https://sites.google.com/view/data-regularized-q. | [] | [
"Continuous Control",
"Data Augmentation",
"Image Augmentation"
] | [] | [
"DeepMind Walker Walk (Images)",
"DeepMind Cheetah Run (Images)",
"DeepMind Cup Catch (Images)"
] | [
"Return"
] | Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels |
In this paper, we study current and upcoming frontiers across the landscape of skeleton-based human action recognition. To begin with, we benchmark state-of-the-art models on the NTU-120 dataset and provide multi-layered assessment of the results. To examine skeleton action recognition 'in the wild', we introduce Skeletics-152, a curated and 3-D pose-annotated subset of RGB videos sourced from Kinetics-700, a large-scale action dataset. The results from benchmarking the top performers of NTU-120 on Skeletics-152 reveal the challenges and domain gap induced by actions 'in the wild'. We extend our study to include out-of-context actions by introducing Skeleton-Mimetics, a dataset derived from the recently introduced Mimetics dataset. Finally, as a new frontier for action recognition, we introduce Metaphorics, a dataset with caption-style annotated YouTube videos of the popular social game Dumb Charades and interpretative dance performances. Overall, our work characterizes the strengths and limitations of existing approaches and datasets. It also provides an assessment of top-performing approaches across a spectrum of activity settings and via the introduced datasets, proposes new frontiers for human action recognition. | [] | [
"Action Recognition",
"Skeleton Based Action Recognition",
"Temporal Action Localization"
] | [] | [
"Skeletics-152",
"Skeleton-Mimetics",
"NTU RGB+D 120"
] | [
"Accuracy (Cross-Subject)",
"Accuracy (%)",
"Accuracy (Cross-Setup)"
] | Quo Vadis, Skeleton Action Recognition ? |
Image guided depth completion is the task of generating a dense depth map from a sparse depth map and a high quality image. In this task, how to fuse the color and depth modalities plays an important role in achieving good performance. This paper proposes a two-branch backbone that consists of a color-dominant branch and a depth-dominant branch to exploit and fuse two modalities thoroughly. More specifically, one branch inputs a color image and a sparse depth map to predict a dense depth map. The other branch takes as inputs the sparse depth map and the previously predicted depth map, and outputs a dense depth map as well. The depth maps predicted from two branches are complimentary to each other and therefore they are adaptively fused. In addition, we also propose a simple geometric convolutional layer to encode 3D geometric cues. The geometric encoded backbone conducts the fusion of different modalities at multiple stages, leading to good depth completion results. We further implement a dilated and accelerated CSPN++ to refine the fused depth map efficiently. The proposed full model ranks 1st in the KITTI depth completion online leaderboard at the time of submission. It also infers much faster than most of the top ranked methods. The code of this work is available at https://github.com/JUGGHM/PENet_ICRA2021. | [] | [
"Depth Completion"
] | [] | [
"KITTI Depth Completion"
] | [
"iMAE",
"RMSE",
"Runtime [ms]",
"MAE",
"iRMSE"
] | PENet: Towards Precise and Efficient Image Guided Depth Completion |
Sequence-to-sequence models have shown strong performance across a broad
range of applications. However, their application to parsing and generating
text usingAbstract Meaning Representation (AMR)has been limited, due to the
relatively limited amount of labeled data and the non-sequential nature of the
AMR graphs. We present a novel training procedure that can lift this limitation
using millions of unlabeled sentences and careful preprocessing of the AMR
graphs. For AMR parsing, our model achieves competitive results of 62.1SMATCH,
the current best score reported without significant use of external semantic
resources. For AMR generation, our model establishes a new state-of-the-art
performance of BLEU 33.8. We present extensive ablative and qualitative
analysis including strong evidence that sequence-based AMR models are robust
against ordering variations of graph-to-sequence conversions. | [] | [
"AMR Parsing",
"Graph-to-Sequence"
] | [] | [
"LDC2015E86"
] | [
"Smatch"
] | Neural AMR: Sequence-to-Sequence Models for Parsing and Generation |
In this paper, we propose a novel approach for text detec- tion in natural
images. Both local and global cues are taken into account for localizing text
lines in a coarse-to-fine pro- cedure. First, a Fully Convolutional Network
(FCN) model is trained to predict the salient map of text regions in a holistic
manner. Then, text line hypotheses are estimated by combining the salient map
and character components. Fi- nally, another FCN classifier is used to predict
the centroid of each character, in order to remove the false hypotheses. The
framework is general for handling text in multiple ori- entations, languages
and fonts. The proposed method con- sistently achieves the state-of-the-art
performance on three text detection benchmarks: MSRA-TD500, ICDAR2015 and
ICDAR2013. | [
"Graph Embeddings"
] | [] | [
"LINE",
"Large-scale Information Network Embedding"
] | [
"ICDAR 2015"
] | [
"F-Measure",
"Recall",
"Precision"
] | Multi-Oriented Text Detection with Fully Convolutional Networks |
We propose a new benchmark corpus to be used for measuring progress in
statistical language modeling. With almost one billion words of training data,
we hope this benchmark will be useful to quickly evaluate novel language
modeling techniques, and to compare their contribution when combined with other
advanced techniques. We show performance of several well-known types of
language models, with the best results achieved with a recurrent neural network
based language model. The baseline unpruned Kneser-Ney 5-gram model achieves
perplexity 67.6; a combination of techniques leads to 35% reduction in
perplexity, or 10% reduction in cross-entropy (bits), over that baseline.
The benchmark is available as a code.google.com project; besides the scripts
needed to rebuild the training/held-out data, it also makes available
log-probability values for each word in each of ten held-out data sets, for
each of the baseline n-gram models. | [] | [
"Language Modelling"
] | [] | [
"One Billion Word"
] | [
"Number of params",
"PPL"
] | One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling |
Domain adaptation aims to transfer knowledge from the sourcedata with annotations to scarcely-labeled data in the target domain,which has attracted a lot of attention in recent years and facilitatedmany multimedia applications. Recent approaches have shown theeffectiveness of using adversarial learning to reduce the distribu-tion discrepancy between the source and target images by aligningdistribution between source and target images at both image and in-stance levels. However, this remains challenging since two domainsmay have distinct background scenes and different objects. More-over, complex combinations of objects and a variety of image stylesdeteriorate the unsupervised cross-domain distribution alignment.To address these challenges, in this paper, we design an end-to-endapproach for unsupervised domain adaptation of object detector.Specifically, we propose a Multi-level Entropy Attention Alignment(MEAA) method that consists of two main components: (1) LocalUncertainty Attentional Alignment (LUAA) module to acceleratethe model better perceiving structure-invariant objects of interestby utilizing information theory to measure the uncertainty of eachlocal region via the entropy of the pixel-wise domain classifierand (2) Multi-level Uncertainty-Aware Context Alignment (MUCA)module to enrich domain-invariant information of relevant objectsbased on the entropy of multi-level domain classifiers. The proposedMEAA is evaluated in four domain-shift object detection scenarios.Experiment results demonstrate state-of-the-art performance onthree challenging scenarios and competitive performance on onebenchmark dataset. | [] | [
"Domain Adaptation",
"Object Detection",
"Unsupervised Domain Adaptation",
"Weakly Supervised Object Detection"
] | [] | [
"Cityscapes-to-Foggy Cityscapes",
"Watercolor2k",
"Clipart1k"
] | [
"mAP",
"MAP"
] | Domain-Adaptive Object Detection via Uncertainty-Aware Distribution Alignment |
The ability to accurately represent sentences is central to language
understanding. We describe a convolutional architecture dubbed the Dynamic
Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of
sentences. The network uses Dynamic k-Max Pooling, a global pooling operation
over linear sequences. The network handles input sentences of varying length
and induces a feature graph over the sentence that is capable of explicitly
capturing short and long-range relations. The network does not rely on a parse
tree and is easily applicable to any language. We test the DCNN in four
experiments: small scale binary and multi-class sentiment prediction, six-way
question classification and Twitter sentiment prediction by distant
supervision. The network achieves excellent performance in the first three
tasks and a greater than 25% error reduction in the last task with respect to
the strongest baseline. | [] | [] | [] | [
"SNLI"
] | [
"% Test Accuracy"
] | A Convolutional Neural Network for Modelling Sentences |
We introduce multigrid Predictive Filter Flow (mgPFF), a framework for
unsupervised learning on videos. The mgPFF takes as input a pair of frames and
outputs per-pixel filters to warp one frame to the other. Compared to optical
flow used for warping frames, mgPFF is more powerful in modeling sub-pixel
movement and dealing with corruption (e.g., motion blur). We develop a
multigrid coarse-to-fine modeling strategy that avoids the requirement of
learning large filters to capture large displacement. This allows us to train
an extremely compact model (4.6MB) which operates in a progressive way over
multiple resolutions with shared weights. We train mgPFF on unsupervised,
free-form videos and show that mgPFF is able to not only estimate long-range
flow for frame reconstruction and detect video shot transitions, but also
readily amendable for video object segmentation and pose tracking, where it
substantially outperforms the published state-of-the-art without bells and
whistles. Moreover, owing to mgPFF's nature of per-pixel filter prediction, we
have the unique opportunity to visualize how each pixel is evolving during
solving these tasks, thus gaining better interpretability. | [] | [
"Optical Flow Estimation",
"Pose Tracking",
"Semantic Segmentation",
"Skeleton Based Action Recognition",
"Video Object Segmentation",
"Video Semantic Segmentation"
] | [] | [
"JHMDB Pose Tracking"
] | [
"[email protected]",
"[email protected]",
"[email protected]",
"[email protected]",
"[email protected]"
] | Multigrid Predictive Filter Flow for Unsupervised Learning on Videos |
The Weisfeiler–Lehman graph kernel exhibits competitive performance in many graph classification tasks. However, its subtree features are not able to capture connected components and cycles, topological features known for characterising graphs. To extract such features, we leverage propagated node label information and transform unweighted graphs into metric ones. This permits us to augment the subtree features with topological information obtained using persistent homology, a concept from topological data analysis. Our method, which we formalise as a generalisation of Weisfeiler–Lehman subtree features, exhibits favourable classification accuracy and its improvements in predictive performance are mainly driven by including cycle information. | [] | [
"Graph Classification",
"Topological Data Analysis"
] | [] | [
"PROTEINS",
"MUTAG"
] | [
"Mean Accuracy",
"Accuracy"
] | A Persistent Weisfeiler–Lehman Procedure for Graph Classification |
We introduce a novel scheme for parsing a piece of text into its Abstract Meaning Representation (AMR): Graph Spanning based Parsing (GSP). One novel characteristic of GSP is that it constructs a parse graph incrementally in a top-down fashion. Starting from the root, at each step, a new node and its connections to existing nodes will be jointly predicted. The output graph spans the nodes by the distance to the root, following the intuition of first grasping the main ideas then digging into more details. The \textit{core semantic first} principle emphasizes capturing the main ideas of a sentence, which is of great interest. We evaluate our model on the latest AMR sembank and achieve the state-of-the-art performance in the sense that no heuristic graph re-categorization is adopted. More importantly, the experiments show that our parser is especially good at obtaining the core semantics. | [] | [
"AMR Parsing"
] | [] | [
"LDC2017T10"
] | [
"Smatch"
] | Core Semantic First: A Top-down Approach for AMR Parsing |
Sparse representation with respect to an overcomplete dictionary is often used when regularizing inverse problems in signal and image processing. In recent years, the Convolutional Sparse Coding (CSC) model, in which the dictionary consists of shift-invariant filters, has gained renewed interest. While this model has been successfully used in some image processing problems, it still falls behind traditional patch-based methods on simple tasks such as denoising. In this work we provide new insights regarding the CSC model and its capability to represent natural images, and suggest a Bayesian connection between this model and its patch-based ancestor. Armed with these observations, we suggest a novel feed-forward network that follows an MMSE approximation process to the CSC model, using strided convolutions. The performance of this supervised architecture is shown to be on par with state of the art methods while using much fewer parameters. | [] | [
"Color Image Denoising",
"Denoising"
] | [] | [
"BSD68 sigma75",
"BSD68 sigma15",
"CBSD68 sigma50",
"BSD68 sigma25"
] | [
"PSNR"
] | Rethinking the CSC Model for Natural Images |
This paper addresses unsupervised domain adaptation, the setting where labeled training data is available on a source domain, but the goal is to have good performance on a target domain with only unlabeled data. Like much of previous work, we seek to align the learned representations of the source and target domains while preserving discriminability. The way we accomplish alignment is by learning to perform auxiliary self-supervised task(s) on both domains simultaneously. Each self-supervised task brings the two domains closer together along the direction relevant to that task. Training this jointly with the main task classifier on the source domain is shown to successfully generalize to the unlabeled target domain. The presented objective is straightforward to implement and easy to optimize. We achieve state-of-the-art results on four out of seven standard benchmarks, and competitive results on segmentation adaptation. We also demonstrate that our method composes well with another popular pixel-level adaptation method. | [] | [
"Domain Adaptation",
"Unsupervised Domain Adaptation"
] | [] | [
"GTAV-to-Cityscapes Labels"
] | [
"mIoU"
] | Unsupervised Domain Adaptation through Self-Supervision |
Extracting geometric features from 3D scans or point clouds is the first step in applications such as registration, reconstruction, and tracking. State-of-the-art methods require computing low-level features as input or extracting patch-based features with limited receptive field. In this work, we present fully-convolutional geometric features, computed in a single pass by a 3D fully-convolutional network. We also present new metric learning losses that dramatically improve performance. Fully-convolutional geometric features are compact, capture broad spatial context, and scale to large scenes. We experimentally validate our approach on both indoor and outdoor datasets. Fully-convolutional geometric features achieve state-of-the-art accuracy without requiring prepossessing, are compact (32 dimensions), and are 600 times faster than the most accurate prior method. | [] | [
"3D Feature Matching",
"3D Point Cloud Matching",
"3D Shape Representation",
"Metric Learning",
"Point Cloud Registration"
] | [] | [
"3DMatch Benchmark"
] | [
"Recall",
"Average Recall"
] | Fully Convolutional Geometric Features |
Deep neural networks have been successfully applied to many real-world applications. However, such successes rely heavily on large amounts of labeled data that is expensive to obtain. Recently, many methods for semi-supervised learning have been proposed and achieved excellent performance. In this study, we propose a new EnAET framework to further improve existing semi-supervised methods with self-supervised information. To our best knowledge, all current semi-supervised methods improve performance with prediction consistency and confidence ideas. We are the first to explore the role of {\bf self-supervised} representations in {\bf semi-supervised} learning under a rich family of transformations. Consequently, our framework can integrate the self-supervised information as a regularization term to further improve {\it all} current semi-supervised methods. In the experiments, we use MixMatch, which is the current state-of-the-art method on semi-supervised learning, as a baseline to test the proposed EnAET framework. Across different datasets, we adopt the same hyper-parameters, which greatly improves the generalization ability of the EnAET framework. Experiment results on different datasets demonstrate that the proposed EnAET framework greatly improves the performance of current semi-supervised algorithms. Moreover, this framework can also improve {\bf supervised learning} by a large margin, including the extremely challenging scenarios with only 10 images per class. The code and experiment records are available in \url{https://github.com/maple-research-lab/EnAET}. | [] | [
"Image Classification",
"Semi-Supervised Image Classification"
] | [] | [
"CIFAR-100, 5000Labels",
"CIFAR-100, 1000 Labels",
"cifar-100, 10000 Labels",
"CIFAR-100",
"CIFAR-10",
"STL-10, 1000 Labels",
"cifar10, 250 Labels",
"SVHN, 250 Labels",
"SVHN, 1000 labels",
"STL-10",
"SVHN",
"CIFAR-10, 4000 Labels"
] | [
"Percentage error",
"Percentage correct",
"Accuracy"
] | EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations |
Graph neural networks have recently emerged as a very effective framework for processing graph-structured data. These models have achieved state-of-the-art performance in many tasks. Most graph neural networks can be described in terms of message passing, vertex update, and readout functions. In this paper, we represent documents as word co-occurrence networks and propose an application of the message passing framework to NLP, the Message Passing Attention network for Document understanding (MPAD). We also propose several hierarchical variants of MPAD. Experiments conducted on 10 standard text classification datasets show that our architectures are competitive with the state-of-the-art. Ablation studies reveal further insights about the impact of the different components on performance. Code is publicly available at: https://github.com/giannisnik/mpad . | [] | [
"Text Classification"
] | [] | [
"BBCSport",
"SST-2 Binary classification",
"Reuters-21578",
"IMDb",
"SST-5 Fine-grained classification",
"TREC-6",
"MPQA"
] | [
"Error",
"Accuracy (2 classes)",
"Accuracy (10 classes)",
"Accuracy"
] | Message Passing Attention Networks for Document Understanding |
Visual dialog is a challenging vision-language task in which a series of questions visually grounded by a given image are answered. To resolve the visual dialog task, a high-level understanding of various multimodal inputs (e.g., question, dialog history, and image) is required. Specifically, it is necessary for an agent to 1) determine the semantic intent of question and 2) align question-relevant textual and visual contents among heterogeneous modality inputs. In this paper, we propose Multi-View Attention Network (MVAN), which leverages multiple views about heterogeneous inputs based on attention mechanisms. MVAN effectively captures the question-relevant information from the dialog history with two complementary modules (i.e., Topic Aggregation and Context Matching), and builds multimodal representations through sequential alignment processes (i.e., Modality Alignment). Experimental results on VisDial v1.0 dataset show the effectiveness of our proposed model, which outperforms the previous state-of-the-art methods with respect to all evaluation metrics. | [] | [
"Visual Dialog"
] | [] | [
"Visual Dialog v1.0 test-std",
"VisDial v0.9 val"
] | [
"MRR (x 100)",
"R@10",
"NDCG (x 100)",
"R@5",
"Mean Rank",
"MRR",
"Mean",
"R@1"
] | Multi-View Attention Network for Visual Dialog |
We present a novel iterative, edit-based approach to unsupervised sentence simplification. Our model is guided by a scoring function involving fluency, simplicity, and meaning preservation. Then, we iteratively perform word and phrase-level edits on the complex sentence. Compared with previous approaches, our model does not require a parallel training set, but is more controllable and interpretable. Experiments on Newsela and WikiLarge datasets show that our approach is nearly as effective as state-of-the-art supervised approaches. | [] | [
"Text Simplification"
] | [] | [
"Newsela",
"TurkCorpus"
] | [
"BLEU",
"SARI (EASSE>=0.2.1)",
"SARI"
] | Iterative Edit-Based Unsupervised Sentence Simplification |
Some downstream NLP tasks exploit discourse dependency trees converted from RST trees. To obtain better discourse dependency trees, we need to improve the accuracy of RST trees at the upper parts of the structures. Thus, we propose a novel neural top-down RST parsing method. Then, we exploit three levels of granularity in a document, paragraphs, sentences and Elementary Discourse Units (EDUs), to parse a document accurately and efficiently. The parsing is done in a top-down manner for each granularity level, by recursively splitting a larger text span into two smaller ones while predicting nuclearity and relation labels for the divided spans. The results on the RST-DT corpus show that our method achieved the state-of-the-art results, 87.0 unlabeled span score, 74.6 nuclearity labeled span score, and the comparable result with the state-of-the-art, 60.0 relation labeled span score. Furthermore, discourse dependency trees converted from our RST trees also achieved the state-of-the-art results, 64.9 unlabeled attachment score and 48.5 labeled attachment score. | [] | [
"Discourse Parsing"
] | [] | [
"RST-DT"
] | [
"RST-Parseval (Relation)",
"RST-Parseval (Span)",
"RST-Parseval (Nuclearity)"
] | Top-Down RST Parsing Utilizing Granularity Levels in Documents |
Contemporary neural networks are limited in their ability to learn from evolving streams of training data. When trained sequentially on new or evolving tasks, their accuracy drops sharply, making them unsuitable for many real-world applications. In this work, we shed light on the causes of this well-known yet unsolved phenomenon - often referred to as catastrophic forgetting - in a class-incremental setup. We show that a combination of simple components and a loss that balances intra-task and inter-task learning can already resolve forgetting to the same extent as more complex measures proposed in literature. Moreover, we identify poor quality of the learned representation as another reason for catastrophic forgetting in class-IL. We show that performance is correlated with secondary class information (dark knowledge) learned by the model and it can be improved by an appropriate regularizer. With these lessons learned, class-incremental learning results on CIFAR-100 and ImageNet improve over the state-of-the-art by a large margin, while keeping the approach simple. | [] | [
"class-incremental learning",
"Continual Learning",
"Incremental Learning"
] | [] | [
"CIFAR-100 - 50 classes + 5 steps of 10 classes",
"ImageNet-100 - 50 classes + 5 steps of 10 classes",
"ImageNet-100 - 50 classes + 10 steps of 5 classes",
"CIFAR-100 - 50 classes + 10 steps of 5 classes",
"ImageNet - 500 classes + 5 steps of 100 classes"
] | [
"Average Incremental Accuracy"
] | Essentials for Class Incremental Learning |
The current strive towards end-to-end trainable computer vision systems imposes major challenges for the task of visual tracking. In contrast to most other vision problems, tracking requires the learning of a robust target-specific appearance model online, during the inference stage. To be end-to-end trainable, the online learning of the target model thus needs to be embedded in the tracking architecture itself. Due to the imposed challenges, the popular Siamese paradigm simply predicts a target feature template, while ignoring the background appearance information during inference. Consequently, the predicted model possesses limited target-background discriminability. We develop an end-to-end tracking architecture, capable of fully exploiting both target and background appearance information for target model prediction. Our architecture is derived from a discriminative learning loss by designing a dedicated optimization process that is capable of predicting a powerful model in only a few iterations. Furthermore, our approach is able to learn key aspects of the discriminative loss itself. The proposed tracker sets a new state-of-the-art on 6 tracking benchmarks, achieving an EAO score of 0.440 on VOT2018, while running at over 40 FPS. The code and models are available at https://github.com/visionml/pytracking. | [] | [
"Visual Object Tracking",
"Visual Tracking"
] | [] | [
"TrackingNet"
] | [
"Normalized Precision",
"Precision",
"Accuracy"
] | Learning Discriminative Model Prediction for Tracking |
3D point cloud generation is of great use for 3D scene modeling and understanding. Real-world 3D object point clouds can be properly described by a collection of low-level and high-level structures such as surfaces, geometric primitives, semantic parts,etc. In fact, there exist many different representations of a 3D object point cloud as a set of point groups. Existing frameworks for point cloud genera-ion either do not consider structure in their proposed solutions, or assume and enforce a specific structure/topology,e.g. a collection of manifolds or surfaces, for the generated point cloud of a 3D object. In this work, we pro-pose a novel decoder that generates a structured point cloud without assuming any specific structure or topology on the underlying point set. Our decoder is softly constrained to generate a point cloud following a hierarchical rooted tree structure. We show that given enough capacity and allowing for redundancies, the proposed decoder is very flexible and able to learn any arbitrary grouping of points including any topology on the point set. We evaluate our decoder on the task of point cloud generation for 3D point cloud shape completion. Combined with encoders from existing frameworks, we show that our proposed decoder significantly outperforms state-of-the-art 3D point cloud completion methods on the Shapenet dataset
| [] | [
"Point Cloud Completion"
] | [] | [
"Completion3D"
] | [
"Chamfer Distance"
] | TopNet: Structural Point Cloud Decoder |
We present a semi-supervised learning framework based on graph embeddings.
Given a graph between instances, we train an embedding for each instance to
jointly predict the class label and the neighborhood context in the graph. We
develop both transductive and inductive variants of our method. In the
transductive variant of our method, the class labels are determined by both the
learned embeddings and input feature vectors, while in the inductive variant,
the embeddings are defined as a parametric function of the feature vectors, so
predictions can be made on instances not seen during training. On a large and
diverse set of benchmark tasks, including text classification, distantly
supervised entity extraction, and entity classification, we show improved
performance over many of the existing models. | [] | [
"Document Classification",
"Entity Extraction using GAN",
"Node Classification",
"Text Classification"
] | [] | [
"Cora",
"NELL",
"Citeseer",
"USA Air-Traffic",
"Pubmed"
] | [
"Accuracy"
] | Revisiting Semi-Supervised Learning with Graph Embeddings |
For visual object tracking, it is difficult to realize an almighty online tracker due to the huge variations of target appearance depending on an image sequence. This paper proposes an online tracking method that adaptively aggregates arbitrary multiple online trackers. The performance of the proposed method is theoretically guaranteed to be comparable to that of the best tracker for any image sequence, although the best expert is unknown during tracking. The experimental study on the large variations of benchmark datasets and aggregated trackers demonstrates that the proposed method can achieve state-of-the-art performance. The code is available at https://github.com/songheony/AAA-journal. | [] | [
"Deblurring",
"Object Tracking",
"Visual Object Tracking"
] | [] | [
"TempleColor128",
"OTB-2015"
] | [
"Precision",
"AUC"
] | AAA: Adaptive Aggregation of Arbitrary Online Trackers with Theoretical Performance Guarantee |
This paper presents a hardness-aware deep metric learning (HDML) framework. Most previous deep metric learning methods employ the hard negative mining strategy to alleviate the lack of informative samples for training. However, this mining strategy only utilizes a subset of training data, which may not be enough to characterize the global geometry of the embedding space comprehensively. To address this problem, we perform linear interpolation on embeddings to adaptively manipulate their hard levels and generate corresponding label-preserving synthetics for recycled training, so that information buried in all samples can be fully exploited and the metric is always challenged with proper difficulty. Our method achieves very competitive performance on the widely used CUB-200-2011, Cars196, and Stanford Online Products datasets. | [] | [
"Image Retrieval",
"Metric Learning"
] | [] | [
" CUB-200-2011",
"CARS196"
] | [
"R@1"
] | Hardness-Aware Deep Metric Learning |
Video temporal action detection aims to temporally localize and recognize the
action in untrimmed videos. Existing one-stage approaches mostly focus on
unifying two subtasks, i.e., localization of action proposals and
classification of each proposal through a fully shared backbone. However, such
design of encapsulating all components of two subtasks in one single network
might restrict the training by ignoring the specialized characteristic of each
subtask. In this paper, we propose a novel Decoupled Single Shot temporal
Action Detection (Decouple-SSAD) method to mitigate such problem by decoupling
the localization and classification in a one-stage scheme. Particularly, two
separate branches are designed in parallel to enable each component to own
representations privately for accurate localization or classification. Each
branch produces a set of action anchor layers by applying deconvolution to the
feature maps of the main stream. Each branch produces a set of feature maps by
applying deconvolution to the feature maps of the main stream. High-level
semantic information from deeper layers is thus incorporated to enhance the
feature representations. We conduct extensive experiments on THUMOS14 dataset
and demonstrate superior performance over state-of-the-art methods. Our code is
available online. | [] | [
"Action Detection"
] | [] | [
"THUMOS’14"
] | [
"mAP [email protected]",
"mAP [email protected]",
"mAP [email protected]",
"mAP [email protected]",
"mAP [email protected]"
] | Decoupling Localization and Classification in Single Shot Temporal Action Detection |
This paper proposes a method for head pose estimation from a single image. Previous methods often predict head poses through landmark or depth estimation and would require more computation than necessary. Our method is based on regression and feature aggregation. For having a compact model, we employ the soft stagewise regression scheme. Existing feature aggregation methods treat inputs as a bag of features and thus ignore their spatial relationship in a feature map. We propose to learn a fine-grained structure mapping for spatially grouping features before aggregation. The fine-grained structure provides part-based information and pooled values. By utilizing learnable and non-learnable importance over the spatial location, different model variants can be generated and form a complementary ensemble. Experiments show that our method outperforms the state-of-the-art methods including both the landmark-free ones and the ones based on landmark or depth estimation. With only a single RGB frame as input, our method even outperforms methods utilizing multi-modality information (RGB-D, RGB-Time) on estimating the yaw angle. Furthermore, the memory overhead of our model is 100 times smaller than those of previous methods.
| [] | [
"Depth Estimation",
"Head Pose Estimation",
"Pose Estimation",
"Regression"
] | [] | [
"AFLW2000",
"BIWI"
] | [
"MAE",
"MAE (trained with other data)"
] | FSA-Net: Learning Fine-Grained Structure Aggregation for Head Pose Estimation From a Single Image |
Recently, the Weisfeiler-Lehman (WL) graph isomorphism test was used to measure the expressive power of graph neural networks (GNN). It was shown that the popular message passing GNN cannot distinguish between graphs that are indistinguishable by the 1-WL test (Morris et al. 2018; Xu et al. 2019). Unfortunately, many simple instances of graphs are indistinguishable by the 1-WL test. In search for more expressive graph learning models we build upon the recent k-order invariant and equivariant graph neural networks (Maron et al. 2019a,b) and present two results: First, we show that such k-order networks can distinguish between non-isomorphic graphs as good as the k-WL tests, which are provably stronger than the 1-WL test for k>2. This makes these models strictly stronger than message passing models. Unfortunately, the higher expressiveness of these models comes with a computational cost of processing high order tensors. Second, setting our goal at building a provably stronger, simple and scalable model we show that a reduced 2-order network containing just scaled identity operator, augmented with a single quadratic operation (matrix multiplication) has a provable 3-WL expressive power. Differently put, we suggest a simple model that interleaves applications of standard Multilayer-Perceptron (MLP) applied to the feature dimension and matrix multiplication. We validate this model by presenting state of the art results on popular graph classification and regression tasks. To the best of our knowledge, this is the first practical invariant/equivariant model with guaranteed 3-WL expressiveness, strictly stronger than message passing models. | [] | [
"Graph Classification",
"Graph Learning",
"Graph Regression",
"Regression"
] | [] | [
"COLLAB",
"NCI109",
"IMDb-B",
"ZINC-500k",
"PROTEINS",
"NCI1",
"IMDb-M",
"MUTAG",
"PTC"
] | [
"MAE",
"Accuracy"
] | Provably Powerful Graph Networks |
Detection of small moving objects is an important research area with applications including monitoring of flying insects, studying their foraging behavior, using insect pollinators to monitor flowering and pollination of crops, surveillance of honeybee colonies, and tracking movement of honeybees. However, due to the lack of distinctive shape and textural details on small objects, direct application of modern object detection methods based on convolutional neural networks (CNNs) shows considerably lower performance. In this paper we propose a method for the detection of small moving objects in videos recorded using unmanned aerial vehicles equipped with standard video cameras. The main steps of the proposed method are video stabilization, background estimation and subtraction, frame segmentation using a CNN, and thresholding the segmented frame. However, for training a CNN it is required that a large labeled dataset is available. Manual labelling of small moving objects in videos is very difficult and time consuming, and such labeled datasets do not exist at the moment. To circumvent this problem, we propose training a CNN using synthetic videos generated by adding small blob-like objects to video sequences with real-world backgrounds. The experimental results on detection of flying honeybees show that by using a combination of classical computer vision techniques and CNNs, as well as synthetic training sets, the proposed approach overcomes the problems associated with direct application of CNNs to the given problem and achieves an average F1-score of 0.86 in tests on real-world videos. | [] | [
"Object Detection",
"Segmentation Of Remote Sensing Imagery",
"Small Object Detection"
] | [] | [
"Bee4Exp Honeybee Detection"
] | [
"Average F1"
] | A Method for Detection of Small Moving Objects in UAV Videos |
In CNN-based object detection methods, region proposal becomes a bottleneck
when objects exhibit significant scale variation, occlusion or truncation. In
addition, these methods mainly focus on 2D object detection and cannot estimate
detailed properties of objects. In this paper, we propose subcategory-aware
CNNs for object detection. We introduce a novel region proposal network that
uses subcategory information to guide the proposal generating process, and a
new detection network for joint detection and subcategory classification. By
using subcategories related to object pose, we achieve state-of-the-art
performance on both detection and pose estimation on commonly used benchmarks. | [] | [
"2D Object Detection",
"Object Detection",
"Pose Estimation",
"Region Proposal"
] | [] | [
"PASCAL VOC 2007"
] | [
"MAP"
] | Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection |
Recent work has shown that CNN-based depth and ego-motion estimators can be learned using unlabelled monocular videos. However, the performance is limited by unidentified moving objects that violate the underlying static scene assumption in geometric image reconstruction. More significantly, due to lack of proper constraints, networks output scale-inconsistent results over different samples, i.e., the ego-motion network cannot provide full camera trajectories over a long video sequence because of the per-frame scale ambiguity. This paper tackles these challenges by proposing a geometry consistency loss for scale-consistent predictions and an induced self-discovered mask for handling moving objects and occlusions. Since we do not leverage multi-task learning like recent works, our framework is much simpler and more efficient. Comprehensive evaluation results demonstrate that our depth estimator achieves the state-of-the-art performance on the KITTI dataset. Moreover, we show that our ego-motion network is able to predict a globally scale-consistent camera trajectory for long video sequences, and the resulting visual odometry accuracy is competitive with the recent model that is trained using stereo videos. To the best of our knowledge, this is the first work to show that deep networks trained using unlabelled monocular videos can predict globally scale-consistent camera trajectories over a long video sequence. | [] | [
"Depth And Camera Motion",
"Depth Estimation",
"Monocular Depth Estimation",
"Visual Odometry"
] | [] | [
"KITTI Eigen split"
] | [
"absolute relative error"
] | Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video |
We present 6-PACK, a deep learning approach to category-level 6D object pose tracking on RGB-D data. Our method tracks in real-time novel object instances of known object categories such as bowls, laptops, and mugs. 6-PACK learns to compactly represent an object by a handful of 3D keypoints, based on which the interframe motion of an object instance can be estimated through keypoint matching. These keypoints are learned end-to-end without manual supervision in order to be most effective for tracking. Our experiments show that our method substantially outperforms existing methods on the NOCS category-level 6D pose estimation benchmark and supports a physical robot to perform simple vision-based closed-loop manipulation tasks. Our code and video are available at https://sites.google.com/view/6packtracking. | [] | [
"6D Pose Estimation",
"6D Pose Estimation using RGBD",
"Pose Estimation",
"Pose Tracking"
] | [] | [
"NOCS-REAL275"
] | [
"Rerr",
"5°5 cm",
"IOU25",
"Terr"
] | 6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints |
The de-facto approach to many vision tasks is to start from pretrained visual representations, typically learned via supervised training on ImageNet. Recent methods have explored unsupervised pretraining to scale to vast quantities of unlabeled images. In contrast, we aim to learn high-quality visual representations from fewer images. To this end, we revisit supervised pretraining, and seek data-efficient alternatives to classification-based pretraining. We propose VirTex -- a pretraining approach using semantically dense captions to learn visual representations. We train convolutional networks from scratch on COCO Captions, and transfer them to downstream recognition tasks including image classification, object detection, and instance segmentation. On all tasks, VirTex yields features that match or exceed those learned on ImageNet -- supervised or unsupervised -- despite using up to ten times fewer images. | [] | [
"Image Captioning",
"Image Classification",
"Instance Segmentation",
"Object Detection",
"Semantic Segmentation"
] | [] | [
"COCO minival",
"COCO test-dev",
"COCO Captions"
] | [
"box AP",
"SPICE",
"AP75",
"CIDER",
"AP50",
"mask AP"
] | VirTex: Learning Visual Representations from Textual Annotations |
Benefiting from the spatial cues embedded in depth images, recent progress on RGB-D saliency detection shows impressive ability on some challenge scenarios. However, there are still two limitations. One hand is that the pooling and upsampling operations in FCNs might cause blur object boundaries. On the other hand, using an additional depth-network to extract depth features might lead to high computation and storage cost. The reliance on depth inputs during testing also limits the practical applications of current RGB-D models. In this paper, we propose a novel collaborative learning framework where edge, depth and saliency are leveraged in a more efficient way, which solves those problems tactfully. The explicitly extracted edge information goes together with saliency to give more emphasis to the salient regions and object boundaries. Depth and saliency learning is innovatively integrated into the high-level feature learning process in a mutual-benefit manner. This strategy enables the network to be free of using extra depth networks and depth inputs to make inference. To this end, it makes our model more lightweight, faster and more versatile. Experiment results on seven benchmark datasets show its superior performance. | [] | [
"Object Detection",
"RGB-D Salient Object Detection",
"RGB Salient Object Detection",
"Saliency Detection",
"Salient Object Detection"
] | [] | [
"NJU2K"
] | [
"Average MAE",
"S-Measure"
] | Accurate RGB-D Salient Object Detection via Collaborative Learning |
We propose a self-supervised method to learn feature representations from videos. A standard approach in traditional self-supervised methods uses positive-negative data pairs to train with contrastive learning strategy. In such a case, different modalities of the same video are treated as positives and video clips from a different video are treated as negatives. Because the spatio-temporal information is important for video representation, we extend the negative samples by introducing intra-negative samples, which are transformed from the same anchor video by breaking temporal relations in video clips. With the proposed Inter-Intra Contrastive (IIC) framework, we can train spatio-temporal convolutional networks to learn video representations. There are many flexible options in our IIC framework and we conduct experiments by using several different configurations. Evaluations are conducted on video retrieval and video recognition tasks using the learned video representation. Our proposed IIC outperforms current state-of-the-art results by a large margin, such as 16.7% and 9.5% points improvements in top-1 accuracy on UCF101 and HMDB51 datasets for video retrieval, respectively. For video recognition, improvements can also be obtained on these two benchmark datasets. Code is available at https://github.com/BestJuly/Inter-intra-video-contrastive-learning. | [] | [
"Action Recognition In Videos",
"Representation Learning",
"Self-Supervised Action Recognition",
"Self-supervised Video Retrieval",
"Video Recognition",
"Video Retrieval"
] | [] | [
"UCF101",
"HMDB51"
] | [
"3-fold Accuracy",
"Pre-Training Dataset",
"Top-1 Accuracy"
] | Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework |
Pre-training models on vast quantities of unlabeled data has emerged as an effective approach to improving accuracy on many NLP tasks. On the other hand, traditional machine translation has a long history of leveraging unlabeled data through noisy channel modeling. The same idea has recently been shown to achieve strong improvements for neural machine translation. Unfortunately, na\"{i}ve noisy channel modeling with modern sequence to sequence models is up to an order of magnitude slower than alternatives. We address this issue by introducing efficient approximations to make inference with the noisy channel approach as fast as strong ensembles while increasing accuracy. We also show that the noisy channel approach can outperform strong pre-training results by achieving a new state of the art on WMT Romanian-English translation. | [] | [
"Machine Translation"
] | [] | [
"WMT2016 Romanian-English"
] | [
"BLEU score"
] | Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling |
Understanding human motion behavior is critical for autonomous moving
platforms (like self-driving cars and social robots) if they are to navigate
human-centric environments. This is challenging because human motion is
inherently multimodal: given a history of human motion paths, there are many
socially plausible ways that people could move in the future. We tackle this
problem by combining tools from sequence prediction and generative adversarial
networks: a recurrent sequence-to-sequence model observes motion histories and
predicts future behavior, using a novel pooling mechanism to aggregate
information across people. We predict socially plausible futures by training
adversarially against a recurrent discriminator, and encourage diverse
predictions with a novel variety loss. Through experiments on several datasets
we demonstrate that our approach outperforms prior work in terms of accuracy,
variety, collision avoidance, and computational complexity. | [] | [
"Motion Forecasting",
"Multi-future Trajectory Prediction",
"Self-Driving Cars",
"Trajectory Forecasting",
"Trajectory Prediction"
] | [] | [
"Stanford Drone",
"ETH/UCY"
] | [
"ADE-8/12 @K = 20",
"FDE(8/12) @K=5",
"FDE-8/12 @K= 20",
"ADE-8/12",
"ADE (8/12) @K=5"
] | Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks |
When building a unified vision system or gradually adding new capabilities to
a system, the usual assumption is that training data for all tasks is always
available. However, as the number of tasks grows, storing and retraining on
such data becomes infeasible. A new problem arises where we add new
capabilities to a Convolutional Neural Network (CNN), but the training data for
its existing capabilities are unavailable. We propose our Learning without
Forgetting method, which uses only new task data to train the network while
preserving the original capabilities. Our method performs favorably compared to
commonly used feature extraction and fine-tuning adaption techniques and
performs similarly to multitask learning that uses original task data we assume
unavailable. A more surprising observation is that Learning without Forgetting
may be able to replace fine-tuning with similar old and new task datasets for
improved new task performance. | [] | [
"Continual Learning"
] | [] | [
"visual domain decathlon (10 tasks)"
] | [
"decathlon discipline (Score)"
] | Learning without Forgetting |
With the proliferation of social media, fashion inspired from celebrities,
reputed designers as well as fashion influencers has shortened the cycle of
fashion design and manufacturing. However, with the explosion of fashion
related content and large number of user generated fashion photos, it is an
arduous task for fashion designers to wade through social media photos and
create a digest of trending fashion. This necessitates deep parsing of fashion
photos on social media to localize and classify multiple fashion items from a
given fashion photo. While object detection competitions such as MSCOCO have
thousands of samples for each of the object categories, it is quite difficult
to get large labeled datasets for fast fashion items. Moreover,
state-of-the-art object detectors do not have any functionality to ingest large
amount of unlabeled data available on social media in order to fine tune object
detectors with labeled datasets. In this work, we show application of a generic
object detector, that can be pretrained in an unsupervised manner, on 24
categories from recently released Open Images V4 dataset. We first train the
base architecture of the object detector using unsupervisd learning on 60K
unlabeled photos from 24 categories gathered from social media, and then
subsequently fine tune it on 8.2K labeled photos from Open Images V4 dataset.
On 300 X 300 image inputs, we achieve 72.7% mAP on a test dataset of 2.4K
photos while performing 11% to 17% better as compared to the state-of-the-art
object detectors. We show that this improvement is due to our choice of
architecture that lets us do unsupervised learning and that performs
significantly better in identifying small objects. | [] | [
"Object Detection"
] | [] | [
"SUN-RGBD val"
] | [
"MAP"
] | How To Extract Fashion Trends From Social Media? A Robust Object Detector With Support For Unsupervised Learning |
Deep learning models have achieved huge success in numerous fields, such as computer vision and natural language processing. However, unlike such fields, it is hard to apply traditional deep learning models on the graph data due to the 'node-orderless' property. Normally, adjacency matrices will cast an artificial and random node-order on the graphs, which renders the performance of deep models on graph classification tasks extremely erratic, and the representations learned by such models lack clear interpretability. To eliminate the unnecessary node-order constraint, we propose a novel model named Isomorphic Neural Network (IsoNN), which learns the graph representation by extracting its isomorphic features via the graph matching between input graph and templates. IsoNN has two main components: graph isomorphic feature extraction component and classification component. The graph isomorphic feature extraction component utilizes a set of subgraph templates as the kernel variables to learn the possible subgraph patterns existing in the input graph and then computes the isomorphic features. A set of permutation matrices is used in the component to break the node-order brought by the matrix representation. Three fully-connected layers are used as the classification component in IsoNN. Extensive experiments are conducted on benchmark datasets, the experimental results can demonstrate the effectiveness of ISONN, especially compared with both classic and state-of-the-art graph classification methods. | [] | [
"Graph Classification",
"Graph Matching",
"Graph Representation Learning",
"Representation Learning"
] | [] | [
"BP-fMRI-97",
"MUTAG",
"HIV-fMRI-77 ",
"PTC",
"HIV-DTI-77",
"HIV-fMRI-77"
] | [
"F1",
"Accuracy"
] | IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification |
In this paper, we are interested in editing text in natural images, which aims to replace or modify a word in the source image with another one while maintaining its realistic look. This task is challenging, as the styles of both background and text need to be preserved so that the edited image is visually indistinguishable from the source image. Specifically, we propose an end-to-end trainable style retention network (SRNet) that consists of three modules: text conversion module, background inpainting module and fusion module. The text conversion module changes the text content of the source image into the target text while keeping the original text style. The background inpainting module erases the original text, and fills the text region with appropriate texture. The fusion module combines the information from the two former modules, and generates the edited text images. To our knowledge, this work is the first attempt to edit text in natural images at the word level. Both visual effects and quantitative results on synthetic and real-world dataset (ICDAR 2013) fully confirm the importance and necessity of modular decomposition. We also conduct extensive experiments to validate the usefulness of our method in various real-world applications such as text image synthesis, augmented reality (AR) translation, information hiding, etc. | [] | [
"Image Generation",
"Image Inpainting",
"Image-to-Image Translation",
"Scene Text Editing"
] | [] | [
"KITTI Object Tracking Evaluation 2012",
"StreetView"
] | [
"Average PSNR",
"SSIM"
] | Editing Text in the Wild |
This paper introduces a new neural structure called FusionNet, which extends
existing attention approaches from three perspectives. First, it puts forward a
novel concept of "history of word" to characterize attention information from
the lowest word-level embedding up to the highest semantic-level
representation. Second, it introduces an improved attention scoring function
that better utilizes the "history of word" concept. Third, it proposes a
fully-aware multi-level attention mechanism to capture the complete information
in one text (such as a question) and exploit it in its counterpart (such as
context or passage) layer by layer. We apply FusionNet to the Stanford Question
Answering Dataset (SQuAD) and it achieves the first position for both single
and ensemble model on the official SQuAD leaderboard at the time of writing
(Oct. 4th, 2017). Meanwhile, we verify the generalization of FusionNet with two
adversarial SQuAD datasets and it sets up the new state-of-the-art on both
datasets: on AddSent, FusionNet increases the best F1 metric from 46.6% to
51.4%; on AddOneSent, FusionNet boosts the best F1 metric from 56.0% to 60.7%. | [] | [
"Question Answering",
"Reading Comprehension"
] | [] | [
"SQuAD1.1 dev",
"SQuAD1.1",
"SQuAD2.0"
] | [
"EM",
"F1"
] | FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension |
We present an end-to-end 3D reconstruction method for a scene by directly regressing a truncated signed distance function (TSDF) from a set of posed RGB images. Traditional approaches to 3D reconstruction rely on an intermediate representation of depth maps prior to estimating a full 3D model of a scene. We hypothesize that a direct regression to 3D is more effective. A 2D CNN extracts features from each image independently which are then back-projected and accumulated into a voxel volume using the camera intrinsics and extrinsics. After accumulation, a 3D CNN refines the accumulated features and predicts the TSDF values. Additionally, semantic segmentation of the 3D model is obtained without significant computation. This approach is evaluated on the Scannet dataset where we significantly outperform state-of-the-art baselines (deep multiview stereo followed by traditional TSDF fusion) both quantitatively and qualitatively. We compare our 3D semantic segmentation to prior methods that use a depth sensor since no previous work attempts the problem with only RGB input. | [] | [
"3D Reconstruction",
"3D Scene Reconstruction",
"3D Semantic Segmentation",
"Depth Estimation",
"Regression",
"Semantic Segmentation"
] | [] | [
"ScanNet"
] | [
"3DIoU",
"RMSE",
"absolute relative error",
"Chamfer Distance",
"L1"
] | Atlas: End-to-End 3D Scene Reconstruction from Posed Images |
Document-level relation extraction requires integrating information within and across multiple sentences of a document and capturing complex interactions between inter-sentence entities. However, effective aggregation of relevant information in the document remains a challenging research question. Existing approaches construct static document-level graphs based on syntactic trees, co-references or heuristics from the unstructured text to model the dependencies. Unlike previous methods that may not be able to capture rich non-local interactions for inference, we propose a novel model that empowers the relational reasoning across sentences by automatically inducing the latent document-level graph. We further develop a refinement strategy, which enables the model to incrementally aggregate relevant information for multi-hop reasoning. Specifically, our model achieves an F1 score of 59.05 on a large-scale document-level dataset (DocRED), significantly improving over the previous results, and also yields new state-of-the-art results on the CDR and GDA dataset. Furthermore, extensive analyses show that the model is able to discover more accurate inter-sentence relations. | [] | [
"Relational Reasoning",
"Relation Extraction"
] | [] | [
"DocRED"
] | [
"Ign F1",
"F1"
] | Reasoning with Latent Structure Refinement for Document-Level Relation Extraction |
We propose an effective framework for the temporal action segmentation task, namely an Action Segment Refinement Framework (ASRF). Our model architecture consists of a long-term feature extractor and two branches: the Action Segmentation Branch (ASB) and the Boundary Regression Branch (BRB). The long-term feature extractor provides shared features for the two branches with a wide temporal receptive field. The ASB classifies video frames with action classes, while the BRB regresses the action boundary probabilities. The action boundaries predicted by the BRB refine the output from the ASB, which results in a significant performance improvement. Our contributions are three-fold: (i) We propose a framework for temporal action segmentation, the ASRF, which divides temporal action segmentation into frame-wise action classification and action boundary regression. Our framework refines frame-level hypotheses of action classes using predicted action boundaries. (ii) We propose a loss function for smoothing the transition of action probabilities, and analyze combinations of various loss functions for temporal action segmentation. (iii) Our framework outperforms state-of-the-art methods on three challenging datasets, offering an improvement of up to 13.7% in terms of segmental edit distance and up to 16.1% in terms of segmental F1 score. Our code will be publicly available soon. | [] | [
"Action Classification",
"Action Classification ",
"Action Segmentation",
"Regression"
] | [] | [
"50 Salads",
"Breakfast",
"GTEA"
] | [
"Acc",
"Edit",
"F1@10%",
"F1@25%",
"F1@50%"
] | Alleviating Over-segmentation Errors by Detecting Action Boundaries |
Despite great progress in supervised semantic segmentation,a large performance drop is usually observed when deploying the model in the wild. Domain adaptation methods tackle the issue by aligning the source domain and the target domain. However, most existing methods attempt to perform the alignment from a holistic view, ignoring the underlying class-level data structure in the target domain. To fully exploit the supervision in the source domain, we propose a fine-grained adversarial learning strategy for class-level feature alignment while preserving the internal structure of semantics across domains. We adopt a fine-grained domain discriminator that not only plays as a domain distinguisher, but also differentiates domains at class level. The traditional binary domain labels are also generalized to domain encodings as the supervision signal to guide the fine-grained feature alignment. An analysis with Class Center Distance (CCD) validates that our fine-grained adversarial strategy achieves better class-level alignment compared to other state-of-the-art methods. Our method is easy to implement and its effectiveness is evaluated on three classical domain adaptation tasks, i.e., GTA5 to Cityscapes, SYNTHIA to Cityscapes and Cityscapes to Cross-City. Large performance gains show that our method outperforms other global feature alignment based and class-wise alignment based counterparts. The code is publicly available at https://github.com/JDAI-CV/FADA. | [] | [
"Domain Adaptation",
"Image-to-Image Translation",
"Semantic Segmentation",
"Synthetic-to-Real Translation"
] | [] | [
"GTAV-to-Cityscapes Labels",
"SYNTHIA-to-Cityscapes"
] | [
"mIoU (13 classes)",
"mIoU"
] | Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation |
The superiority of deeply learned pedestrian representations has been
reported in very recent literature of person re-identification (re-ID). In this
paper, we consider the more pragmatic issue of learning a deep feature with no
or only a few labels. We propose a progressive unsupervised learning (PUL)
method to transfer pretrained deep representations to unseen domains. Our
method is easy to implement and can be viewed as an effective baseline for
unsupervised re-ID feature learning. Specifically, PUL iterates between 1)
pedestrian clustering and 2) fine-tuning of the convolutional neural network
(CNN) to improve the original model trained on the irrelevant labeled dataset.
Since the clustering results can be very noisy, we add a selection operation
between the clustering and fine-tuning. At the beginning when the model is
weak, CNN is fine-tuned on a small amount of reliable examples which locate
near to cluster centroids in the feature space. As the model becomes stronger
in subsequent iterations, more images are being adaptively selected as CNN
training samples. Progressively, pedestrian clustering and the CNN model are
improved simultaneously until algorithm convergence. This process is naturally
formulated as self-paced learning. We then point out promising directions that
may lead to further improvement. Extensive experiments on three large-scale
re-ID datasets demonstrate that PUL outputs discriminative features that
improve the re-ID accuracy. | [] | [
"Person Re-Identification",
"Unsupervised Person Re-Identification"
] | [] | [
"DukeMTMC-reID",
"Market-1501"
] | [
"Rank-1",
"Rank-10",
"Rank-5",
"MAP"
] | Unsupervised Person Re-identification: Clustering and Fine-tuning |
The present study proposes a deep learning model, named DeepSleepNet, for
automatic sleep stage scoring based on raw single-channel EEG. Most of the
existing methods rely on hand-engineered features which require prior knowledge
of sleep analysis. Only a few of them encode the temporal information such as
transition rules, which is important for identifying the next sleep stages,
into the extracted features. In the proposed model, we utilize Convolutional
Neural Networks to extract time-invariant features, and bidirectional-Long
Short-Term Memory to learn transition rules among sleep stages automatically
from EEG epochs. We implement a two-step training algorithm to train our model
efficiently. We evaluated our model using different single-channel EEGs
(F4-EOG(Left), Fpz-Cz and Pz-Oz) from two public sleep datasets, that have
different properties (e.g., sampling rate) and scoring standards (AASM and
R&K). The results showed that our model achieved similar overall accuracy and
macro F1-score (MASS: 86.2%-81.7, Sleep-EDF: 82.0%-76.9) compared to the
state-of-the-art methods (MASS: 85.9%-80.5, Sleep-EDF: 78.9%-73.7) on both
datasets. This demonstrated that, without changing the model architecture and
the training algorithm, our model could automatically learn features for sleep
stage scoring from different raw single-channel EEGs from different datasets
without utilizing any hand-engineered features. | [] | [
"EEG",
"Sleep Stage Detection"
] | [] | [
"MASS SS3",
"Sleep-EDF"
] | [
"Cohen's kappa",
"Macro-F1",
"Accuracy"
] | DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG |
Person re-identification (reID) is an important task that requires to
retrieve a person's images from an image dataset, given one image of the person
of interest. For learning robust person features, the pose variation of person
images is one of the key challenges. Existing works targeting the problem
either perform human alignment, or learn human-region-based representations.
Extra pose information and computational cost is generally required for
inference. To solve this issue, a Feature Distilling Generative Adversarial
Network (FD-GAN) is proposed for learning identity-related and pose-unrelated
representations. It is a novel framework based on a Siamese structure with
multiple novel discriminators on human poses and identities. In addition to the
discriminators, a novel same-pose loss is also integrated, which requires
appearance of a same person's generated images to be similar. After learning
pose-unrelated person features with pose guidance, no auxiliary pose
information and additional computational cost is required during testing. Our
proposed FD-GAN achieves state-of-the-art performance on three person reID
datasets, which demonstrates that the effectiveness and robust feature
distilling capability of the proposed FD-GAN. | [] | [
"Person Re-Identification"
] | [] | [
"DukeMTMC-reID",
"Market-1501",
"CUHK03"
] | [
"Rank-1",
"MAP"
] | FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification |
Unsupervised learning of syntactic structure is typically performed using
generative models with discrete latent variables and multinomial parameters. In
most cases, these models have not leveraged continuous word representations. In
this work, we propose a novel generative model that jointly learns discrete
syntactic structure and continuous word representations in an unsupervised
fashion by cascading an invertible neural network with a structured generative
prior. We show that the invertibility condition allows for efficient exact
inference and marginal likelihood computation in our model so long as the prior
is well-behaved. In experiments we instantiate our approach with both Markov
and tree-structured priors, evaluating on two tasks: part-of-speech (POS)
induction, and unsupervised dependency parsing without gold POS annotation. On
the Penn Treebank, our Markov-structured model surpasses state-of-the-art
results on POS induction. Similarly, we find that our tree-structured model
achieves state-of-the-art performance on unsupervised dependency parsing for
the difficult training condition where neither gold POS annotation nor
punctuation-based constraints are available. | [] | [
"Constituency Grammar Induction",
"Dependency Parsing",
"Unsupervised Dependency Parsing"
] | [] | [
"PTB"
] | [
"Mean F1 (WSJ10)",
"Mean F1 (WSJ)"
] | Unsupervised Learning of Syntactic Structure with Invertible Neural Projections |
Deep learning is currently playing a crucial role toward higher levels of artificial intelligence. This paradigm allows neural networks to learn complex and abstract representations, that are progressively obtained by combining simpler ones. Nevertheless, the internal "black-box" representations automatically discovered by current neural architectures often suffer from a lack of interpretability, making of primary interest the study of explainable machine learning techniques. This paper summarizes our recent efforts to develop a more interpretable neural model for directly processing speech from the raw waveform. In particular, we propose SincNet, a novel Convolutional Neural Network (CNN) that encourages the first layer to discover more meaningful filters by exploiting parametrized sinc functions. In contrast to standard CNNs, which learn all the elements of each filter, only low and high cutoff frequencies of band-pass filters are directly learned from data. This inductive bias offers a very compact way to derive a customized filter-bank front-end, that only depends on some parameters with a clear physical meaning. Our experiments, conducted on both speaker and speech recognition, show that the proposed architecture converges faster, performs better, and is more interpretable than standard CNNs. | [] | [
"Distant Speech Recognition",
"Speech Recognition"
] | [] | [
"DIRHA English WSJ"
] | [
"Word Error Rate (WER)"
] | Interpretable Convolutional Filters with SincNet |
Model-based human pose estimation is currently approached through two different paradigms. Optimization-based methods fit a parametric body model to 2D observations in an iterative manner, leading to accurate image-model alignments, but are often slow and sensitive to the initialization. In contrast, regression-based methods, that use a deep network to directly estimate the model parameters from pixels, tend to provide reasonable, but not pixel accurate, results while requiring huge amounts of supervision. In this work, instead of investigating which approach is better, our key insight is that the two paradigms can form a strong collaboration. A reasonable, directly regressed estimate from the network can initialize the iterative optimization making the fitting faster and more accurate. Similarly, a pixel accurate fit from iterative optimization can act as strong supervision for the network. This is the core of our proposed approach SPIN (SMPL oPtimization IN the loop). The deep network initializes an iterative optimization routine that fits the body model to 2D joints within the training loop, and the fitted estimate is subsequently used to supervise the network. Our approach is self-improving by nature, since better network estimates can lead the optimization to better solutions, while more accurate optimization fits provide better supervision for the network. We demonstrate the effectiveness of our approach in different settings, where 3D ground truth is scarce, or not available, and we consistently outperform the state-of-the-art model-based pose estimation approaches by significant margins. The project website with videos, results, and code can be found at https://seas.upenn.edu/~nkolot/projects/spin. | [] | [
"3D Human Pose Estimation",
"Pose Estimation",
"Regression"
] | [] | [
"3D Poses in the Wild Challenge",
"MPI-INF-3DHP",
"3DPW"
] | [
"PA-MPJPE",
"MPVPE",
"MPJPE",
"MJPE",
"AUC",
"3DPCK",
"MPJAE"
] | Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop |
Image clustering is a crucial but challenging task in machine learning and computer vision. Existing methods often ignore the combination between feature learning and clustering. To tackle this problem, we propose Deep Adaptive Clustering (DAC) that recasts the clustering problem into a binary pairwise-classification framework to judge whether pairs of images belong to the same clusters. In DAC, the similarities are calculated as the cosine distance between label features of images which are generated by a deep convolutional network (ConvNet). By introducing a constraint into DAC, the learned label features tend to be one-hot vectors that can be utilized for clustering images. The main challenge is that the ground-truth similarities are unknown in image clustering. We handle this issue by presenting an alternating iterative Adaptive Learning algorithm where each iteration alternately selects labeled samples and trains the ConvNet. Conclusively, images are automatically clustered based on the label features. Experimental results show that DAC achieves state-of-the-art performance on five popular datasets, e.g., yielding 97.75% clustering accuracy on MNIST, 52.18% on CIFAR-10 and 46.99% on STL-10.
| [] | [
"Image Clustering"
] | [] | [
"Imagenet-dog-15",
"CIFAR-100",
"CIFAR-10",
"Tiny-ImageNet",
"ImageNet-10",
"STL-10"
] | [
"Train set",
"Train Split",
"ARI",
"Backbone",
"Train Set",
"NMI",
"Accuracy"
] | Deep Adaptive Image Clustering |
Although unsupervised person re-identification (RE-ID) has drawn increasing
research attentions due to its potential to address the scalability problem of
supervised RE-ID models, it is very challenging to learn discriminative
information in the absence of pairwise labels across disjoint camera views. To
overcome this problem, we propose a deep model for the soft multilabel learning
for unsupervised RE-ID. The idea is to learn a soft multilabel (real-valued
label likelihood vector) for each unlabeled person by comparing (and
representing) the unlabeled person with a set of known reference persons from
an auxiliary domain. We propose the soft multilabel-guided hard negative mining
to learn a discriminative embedding for the unlabeled target domain by
exploring the similarity consistency of the visual features and the soft
multilabels of unlabeled target pairs. Since most target pairs are cross-view
pairs, we develop the cross-view consistent soft multilabel learning to achieve
the learning goal that the soft multilabels are consistently good across
different camera views. To enable effecient soft multilabel learning, we
introduce the reference agent learning to represent each reference person by a
reference agent in a joint embedding. We evaluate our unified deep model on
Market-1501 and DukeMTMC-reID. Our model outperforms the state-of-the-art
unsupervised RE-ID methods by clear margins. Code is available at
https://github.com/KovenYu/MAR. | [] | [
"Person Re-Identification",
"Unsupervised Person Re-Identification"
] | [] | [
"DukeMTMC-reID",
"Market-1501"
] | [
"Rank-1",
"Rank-5",
"MAP"
] | Unsupervised Person Re-identification by Soft Multilabel Learning |
Arbitrary-oriented objects widely appear in natural scenes, aerial photographs, remote sensing images, etc., thus arbitrary-oriented object detection has received considerable attention. Many current rotation detectors use plenty of anchors with different orientations to achieve spatial alignment with ground truth boxes, then Intersection-over-Union (IoU) is applied to sample the positive and negative candidates for training. However, we observe that the selected positive anchors cannot always ensure accurate detections after regression, while some negative samples can achieve accurate localization. It indicates that the quality assessment of anchors through IoU is not appropriate, and this further lead to inconsistency between classification confidence and localization accuracy. In this paper, we propose a dynamic anchor learning (DAL) method, which utilizes the newly defined matching degree to comprehensively evaluate the localization potential of the anchors and carry out a more efficient label assignment process. In this way, the detector can dynamically select high-quality anchors to achieve accurate object detection, and the divergence between classification and regression will be alleviated. With the newly introduced DAL, we achieve superior detection performance for arbitrary-oriented objects with only a few horizontal preset anchors. Experimental results on three remote sensing datasets HRSC2016, DOTA, UCAS-AOD as well as a scene text dataset ICDAR 2015 show that our method achieves substantial improvement compared with the baseline model. Besides, our approach is also universal for object detection using horizontal bound box. The code and models are available at https://github.com/ming71/DAL. | [] | [
"Multi-Oriented Scene Text Detection",
"Object Detection In Aerial Images",
"Regression"
] | [] | [
"ICDAR2015",
"DOTA"
] | [
"F-Measure",
"mAP"
] | Dynamic Anchor Learning for Arbitrary-Oriented Object Detection |
In graph instance representation learning, both the diverse graph instance sizes and the graph node orderless property have been the major obstacles that render existing representation learning models fail to work. In this paper, we will examine the effectiveness of GRAPH-BERT on graph instance representation learning, which was designed for node representation learning tasks originally. To adapt GRAPH-BERT to the new problem settings, we re-design it with a segmented architecture instead, which is also named as SEG-BERT (Segmented GRAPH-BERT) for reference simplicity in this paper. SEG-BERT involves no node-order-variant inputs or functional components anymore, and it can handle the graph node orderless property naturally. What's more, SEG-BERT has a segmented architecture and introduces three different strategies to unify the graph instance sizes, i.e., full-input, padding/pruning and segment shifting, respectively. SEG-BERT is pre-trainable in an unsupervised manner, which can be further transferred to new tasks directly or with necessary fine-tuning. We have tested the effectiveness of SEG-BERT with experiments on seven graph instance benchmark datasets, and SEG-BERT can out-perform the comparison methods on six out of them with significant performance advantages. | [] | [
"Graph Classification",
"Representation Learning"
] | [] | [
"COLLAB",
"IMDb-B",
"PROTEINS",
"IMDb-M",
"MUTAG",
"PTC"
] | [
"Accuracy"
] | Segmented Graph-Bert for Graph Instance Modeling |
Several deep learning models have been proposed for question answering.
However, due to their single-pass nature, they have no way to recover from
local maxima corresponding to incorrect answers. To address this problem, we
introduce the Dynamic Coattention Network (DCN) for question answering. The DCN
first fuses co-dependent representations of the question and the document in
order to focus on relevant parts of both. Then a dynamic pointing decoder
iterates over potential answer spans. This iterative procedure enables the
model to recover from initial local maxima corresponding to incorrect answers.
On the Stanford question answering dataset, a single DCN model improves the
previous state of the art from 71.0% F1 to 75.9%, while a DCN ensemble obtains
80.4% F1. | [] | [
"Question Answering"
] | [] | [
"SQuAD1.1 dev",
"SQuAD1.1"
] | [
"EM",
"F1"
] | Dynamic Coattention Networks For Question Answering |
Spiking neural networks (SNNs) can be used in low-power and embedded systems (such as emerging neuromorphic chips) due to their event-based nature. Also, they have the advantage of low computation cost in contrast to conventional artificial neural networks (ANNs), while preserving ANN's properties. However, temporal coding in layers of convolutional spiking neural networks and other types of SNNs has yet to be studied. In this paper, we provide insight into spatio-temporal feature extraction of convolutional SNNs in experiments designed to exploit this property. The shallow convolutional SNN outperforms state-of-the-art spatio-temporal feature extractor methods such as C3D, ConvLstm, and similar networks. Furthermore, we present a new deep spiking architecture to tackle real-world problems (in particular classification tasks) which achieved superior performance compared to other SNN methods on NMNIST (99.6%), DVS-CIFAR10 (69.2%) and DVS-Gesture (96.7%) and ANN methods on UCF-101 (42.1%) and HMDB-51 (21.5%) datasets. It is also worth noting that the training process is implemented based on variation of spatio-temporal backpropagation explained in the paper. | [] | [
"Activity Recognition In Videos",
"Event data classification",
"Image Classification",
"Video Classification"
] | [] | [
"CIFAR10-DVS",
"MNIST"
] | [
"Percentage error",
"Accuracy"
] | Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction |
In this paper, we present a simple and efficient method for training deep
neural networks in a semi-supervised setting where only a small portion of
training data is labeled. We introduce self-ensembling, where we form a
consensus prediction of the unknown labels using the outputs of the
network-in-training on different epochs, and most importantly, under different
regularization and input augmentation conditions. This ensemble prediction can
be expected to be a better predictor for the unknown labels than the output of
the network at the most recent training epoch, and can thus be used as a target
for training. Using our method, we set new records for two standard
semi-supervised learning benchmarks, reducing the (non-augmented)
classification error rate from 18.44% to 7.05% in SVHN with 500 labels and from
18.63% to 16.55% in CIFAR-10 with 4000 labels, and further to 5.12% and 12.16%
by enabling the standard augmentations. We additionally obtain a clear
improvement in CIFAR-100 classification accuracy by using random images from
the Tiny Images dataset as unlabeled extra inputs during training. Finally, we
demonstrate good tolerance to incorrect labels. | [] | [
"Semi-Supervised Image Classification"
] | [] | [
"cifar-100, 10000 Labels",
"CIFAR-10, 250 Labels",
"CIFAR-10, 4000 Labels",
"SVHN, 1000 labels"
] | [
"Accuracy"
] | Temporal Ensembling for Semi-Supervised Learning |
To learn intrinsic low-dimensional structures from high-dimensional data that most discriminate between classes, we propose the principle of Maximal Coding Rate Reduction ($\text{MCR}^2$), an information-theoretic measure that maximizes the coding rate difference between the whole dataset and the sum of each individual class. We clarify its relationships with most existing frameworks such as cross-entropy, information bottleneck, information gain, contractive and contrastive learning, and provide theoretical guarantees for learning diverse and discriminative features. The coding rate can be accurately computed from finite samples of degenerate subspace-like distributions and can learn intrinsic representations in supervised, self-supervised, and unsupervised settings in a unified manner. Empirically, the representations learned using this principle alone are significantly more robust to label corruptions in classification than those using cross-entropy, and can lead to state-of-the-art results in clustering mixed data from self-learned invariant features. | [] | [
"Image Clustering"
] | [] | [
"STL-10"
] | [
"NMI",
"Accuracy"
] | Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction |
Deep convolutional neural networks, assisted by architectural design strategies, make extensive use of data augmentation techniques and layers with a high number of feature maps to embed object transformations. That is highly inefficient and for large datasets implies a massive redundancy of features detectors. Even though capsules networks are still in their infancy, they constitute a promising solution to extend current convolutional networks and endow artificial visual perception with a process to encode more efficiently all feature affine transformations. Indeed, a properly working capsule network should theoretically achieve higher results with a considerably lower number of parameters count due to intrinsic capability to generalize to novel viewpoints. Nevertheless, little attention has been given to this relevant aspect. In this paper, we investigate the efficiency of capsule networks and, pushing their capacity to the limits with an extreme architecture with barely 160K parameters, we prove that the proposed architecture is still able to achieve state-of-the-art results on three different datasets with only 2% of the original CapsNet parameters. Moreover, we replace dynamic routing with a novel non-iterative, highly parallelizable routing algorithm that can easily cope with a reduced number of capsules. Extensive experimentation with other capsule implementations has proved the effectiveness of our methodology and the capability of capsule networks to efficiently embed visual representations more prone to generalization. | [] | [
"Data Augmentation",
"Image Classification"
] | [] | [
"MNIST",
"smallNORB"
] | [
"Percentage error",
"Classification Error",
"Trainable Parameters",
"Accuracy"
] | Efficient-CapsNet: Capsule Network with Self-Attention Routing |
This paper presents Task 4 of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 challenge and provides a first analysis of the challenge results. The task is a follow-up to Task 4 of DCASE 2018, and involves training systems for large-scale detection of sound events using a combination of weakly labeled data, i.e. training labels without time boundaries,and strongly-labeled synthesized data. The paper introduces Domestic Environment Sound Event Detection (DESED) dataset mixing a part of last year dataset and an additional synthetic, strongly labeled, dataset provided this year that we’ll describe more in de-tail. We also report the performance of the submitted systems on the official evaluation (test) and development sets as well as several additional datasets. The best systems from this year outperform last year’s winning system by about 10% points in terms of F-measure. | [] | [
"Sound Event Detection"
] | [] | [
"DESED"
] | [
"event-based F1 score"
] | Sound event detection in domestic environments withweakly labeled data and soundscape synthesis |
This technical report presents a brief description of our submission to the dense video captioning task of ActivityNet Challenge 2020. Our approach follows a two-stage pipeline: first, we extract a set of temporal event proposals; then we propose a multi-event captioning model to capture the event-level temporal relationships and effectively fuse the multi-modal information. Our approach achieves a 9.28 METEOR score on the test set. | [] | [
"Dense Video Captioning",
"Video Captioning"
] | [] | [
"ActivityNet Captions"
] | [
"METEOR"
] | Dense-Captioning Events in Videos: SYSU Submission to ActivityNet Challenge 2020 |
Graph Neural Networks (GNNs) have achieved promising performance on a wide range of graph-based tasks. Despite their success, one severe limitation of GNNs is the over-smoothing issue (indistinguishable representations of nodes in different classes). In this work, we present a systematic and quantitative study on the over-smoothing issue of GNNs. First, we introduce two quantitative metrics, MAD and MADGap, to measure the smoothness and over-smoothness of the graph nodes representations, respectively. Then, we verify that smoothing is the nature of GNNs and the critical factor leading to over-smoothness is the low information-to-noise ratio of the message received by the nodes, which is partially determined by the graph topology. Finally, we propose two methods to alleviate the over-smoothing issue from the topological view: (1) MADReg which adds a MADGap-based regularizer to the training objective;(2) AdaGraph which optimizes the graph topology based on the model predictions. Extensive experiments on 7 widely-used graph datasets with 10 typical GNN models show that the two proposed methods are effective for relieving the over-smoothing issue, thus improving the performance of various GNN models. | [] | [
"Node Classification"
] | [] | [
"Cora",
"Pubmed",
"Citeseer"
] | [
"Accuracy"
] | Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View |
Previous adversarial training raises model robustness under the compromise of accuracy on natural data. In this paper, our target is to reduce natural accuracy degradation. We use the model logits from one clean model $\mathcal{M}^{natural}$ to guide learning of the robust model $\mathcal{M}^{robust}$, taking into consideration that logits from the well trained clean model $\mathcal{M}^{natural}$ embed the most discriminative features of natural data, {\it e.g.}, generalizable classifier boundary. Our solution is to constrain logits from the robust model $\mathcal{M}^{robust}$ that takes adversarial examples as input and make it similar to those from a clean model $\mathcal{M}^{natural}$ fed with corresponding natural data. It lets $\mathcal{M}^{robust}$ inherit the classifier boundary of $\mathcal{M}^{natural}$. Thus, we name our method Boundary Guided Adversarial Training (BGAT). Moreover, we generalize BGAT to Learnable Boundary Guided Adversarial Training (LBGAT) by training $\mathcal{M}^{natural}$ and $\mathcal{M}^{robust}$ simultaneously and collaboratively to learn one most robustness-friendly classifier boundary for the strongest robustness. Extensive experiments are conducted on CIFAR-10, CIFAR-100, and challenging Tiny ImageNet datasets. Along with other state-of-the-art adversarial training approaches, {\it e.g.}, Adversarial Logit Pairing (ALP) and TRADES, the performance is further enhanced. | [] | [
"Adversarial Defense"
] | [] | [
"CIFAR-100"
] | [
"autoattack"
] | Learnable Boundary Guided Adversarial Training |
We address semi-supervised video object segmentation, the task of
automatically generating accurate and consistent pixel masks for objects in a
video sequence, given the first-frame ground truth annotations. Towards this
goal, we present the PReMVOS algorithm (Proposal-generation, Refinement and
Merging for Video Object Segmentation). Our method separates this problem into
two steps, first generating a set of accurate object segmentation mask
proposals for each video frame and then selecting and merging these proposals
into accurate and temporally consistent pixel-wise object tracks over a video
sequence in a way which is designed to specifically tackle the difficult
challenges involved with segmenting multiple objects across a video sequence.
Our approach surpasses all previous state-of-the-art results on the DAVIS 2017
video object segmentation benchmark with a J & F mean score of 71.6 on the
test-dev dataset, and achieves first place in both the DAVIS 2018 Video Object
Segmentation Challenge and the YouTube-VOS 1st Large-scale Video Object
Segmentation Challenge. | [] | [
"Semantic Segmentation",
"Semi-Supervised Video Object Segmentation",
"Video Object Segmentation",
"Video Semantic Segmentation",
"Youtube-VOS"
] | [] | [
"DAVIS 2017 (val)",
"DAVIS 2017 (test-dev)",
"DAVIS 2016"
] | [
"F-measure (Decay)",
"Jaccard (Mean)",
"F-measure (Recall)",
"Jaccard (Decay)",
"Jaccard (Recall)",
"F-measure (Mean)",
"J&F"
] | PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation |
Semantic graphs, such as WordNet, are resources which curate natural language
on two distinguishable layers. On the local level, individual relations between
synsets (semantic building blocks) such as hypernymy and meronymy enhance our
understanding of the words used to express their meanings. Globally, analysis
of graph-theoretic properties of the entire net sheds light on the structure of
human language as a whole. In this paper, we combine global and local
properties of semantic graphs through the framework of Max-Margin Markov Graph
Models (M3GM), a novel extension of Exponential Random Graph Model (ERGM) that
scales to large multi-relational graphs. We demonstrate how such global
modeling improves performance on the local task of predicting semantic
relations between synsets, yielding new state-of-the-art results on the WN18RR
dataset, a challenging version of WordNet link prediction in which "easy"
reciprocal cases are removed. In addition, the M3GM model identifies
multirelational motifs that are characteristic of well-formed lexical semantic
ontologies. | [] | [
"Link Prediction"
] | [] | [
"WN18RR"
] | [
"Hits@10",
"MRR",
"Hits@1"
] | Predicting Semantic Relations using Global Graph Properties |
In this paper, we propose a new rich resource enhanced AMR aligner which
produces multiple alignments and a new transition system for AMR parsing along
with its oracle parser. Our aligner is further tuned by our oracle parser via
picking the alignment that leads to the highest-scored achievable AMR graph.
Experimental results show that our aligner outperforms the rule-based aligner
in previous work by achieving higher alignment F1 score and consistently
improving two open-sourced AMR parsers. Based on our aligner and transition
system, we develop a transition-based AMR parser that parses a sentence into
its AMR graph directly. An ensemble of our parsers with only words and POS tags
as input leads to 68.4 Smatch F1 score. | [] | [
"AMR Parsing"
] | [] | [
"LDC2014T12:",
"LDC2014T12"
] | [
"F1 Newswire",
"F1 Full"
] | An AMR Aligner Tuned by Transition-based Parser |
In this paper, we propose PointRCNN for 3D object detection from raw point cloud. The whole framework is composed of two stages: stage-1 for the bottom-up 3D proposal generation and stage-2 for refining proposals in the canonical coordinates to obtain the final detection results. Instead of generating proposals from RGB image or projecting point cloud to bird's view or voxels as previous methods do, our stage-1 sub-network directly generates a small number of high-quality 3D proposals from point cloud in a bottom-up manner via segmenting the point cloud of the whole scene into foreground points and background. The stage-2 sub-network transforms the pooled points of each proposal to canonical coordinates to learn better local spatial features, which is combined with global semantic features of each point learned in stage-1 for accurate box refinement and confidence prediction. Extensive experiments on the 3D detection benchmark of KITTI dataset show that our proposed architecture outperforms state-of-the-art methods with remarkable margins by using only point cloud as input. The code is available at https://github.com/sshaoshuai/PointRCNN. | [] | [
"3D Object Detection",
"Object Detection",
"Object Proposal Generation"
] | [] | [
"KITTI Cars Hard",
"KITTI Cyclists Hard",
"KITTI Cars Moderate",
"KITTI Cyclists Moderate",
"KITTI Cyclists Easy",
"KITTI Cars Easy"
] | [
"AP"
] | PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud |
This paper introduces an extremely efficient CNN architecture named DFANet
for semantic segmentation under resource constraints. Our proposed network
starts from a single lightweight backbone and aggregates discriminative
features through sub-network and sub-stage cascade respectively. Based on the
multi-scale feature propagation, DFANet substantially reduces the number of
parameters, but still obtains sufficient receptive field and enhances the model
learning ability, which strikes a balance between the speed and segmentation
performance. Experiments on Cityscapes and CamVid datasets demonstrate the
superior performance of DFANet with 8$\times$ less FLOPs and 2$\times$ faster
than the existing state-of-the-art real-time semantic segmentation methods
while providing comparable accuracy. Specifically, it achieves 70.3\% Mean IOU
on the Cityscapes test dataset with only 1.7 GFLOPs and a speed of 160 FPS on
one NVIDIA Titan X card, and 71.3\% Mean IOU with 3.4 GFLOPs while inferring on
a higher resolution image. | [] | [
"Real-Time Semantic Segmentation",
"Semantic Segmentation"
] | [] | [
"CamVid",
"Cityscapes test"
] | [
"Mean IoU (class)",
"Mean IoU"
] | DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation |
Visual Question Answering (VQA) is the task of answering questions about an image. Some VQA models often exploit unimodal biases to provide the correct answer without using the image information. As a result, they suffer from a huge drop in performance when evaluated on data outside their training set distribution. This critical issue makes them unsuitable for real-world settings. We propose RUBi, a new learning strategy to reduce biases in any VQA model. It reduces the importance of the most biased examples, i.e. examples that can be correctly classified without looking at the image. It implicitly forces the VQA model to use the two input modalities instead of relying on statistical regularities between the question and the answer. We leverage a question-only model that captures the language biases by identifying when these unwanted regularities are used. It prevents the base VQA model from learning them by influencing its predictions. This leads to dynamically adjusting the loss in order to compensate for biases. We validate our contributions by surpassing the current state-of-the-art results on VQA-CP v2. This dataset is specifically designed to assess the robustness of VQA models when exposed to different question biases at test time than what was seen during training. Our code is available: github.com/cdancette/rubi.bootstrap.pytorch | [] | [
"Question Answering",
"Visual Question Answering"
] | [] | [
"VQA-CP",
"VQA v2 test-dev"
] | [
"Score",
"Accuracy"
] | RUBi: Reducing Unimodal Biases in Visual Question Answering |
This review introduces a novel deformable image registration paradigm that exploits Markov random field formulation and powerful discrete optimization algorithms. We express deformable registration as a minimal cost graph problem, where nodes correspond to the deformation grid, a node's connectivity corresponds to regularization constraints, and labels correspond to 3D deformations. To cope with both iconic and geometric (landmark-based) registration, we introduce two graphical models, one for each subproblem. The two graphs share interconnected variables, leading to a modular, powerful, and flexible formulation that can account for arbitrary image-matching criteria, various local deformation models, and regularization constraints. To cope with the corresponding optimization problem, we adopt two optimization strategies: a computationally efficient one and a tight relaxation alternative. Promising results demonstrate the potential of this approach. Discrete methods are an important new trend in medical image registration, as they provide several improvements over the more traditional continuous methods. This is illustrated with several key examples where the presented framework outperforms existing general-purpose registration methods in terms of both performance and computational complexity. Our methods become of particular interest in applications where computation time is a critical issue, as in intraoperative imaging, or where the huge variation in data demands complex and application-specific matching criteria, as in large-scale multimodal population studies. The proposed registration framework, along with a graphical interface and corresponding publications, is available for download for research purposes (for Windows and Linux platforms) from http://www.mrf-registration.net. | [] | [
"BIRL",
"Deformable Medical Image Registration",
"Image Registration",
"Medical Image Registration"
] | [] | [
"CIMA-10k"
] | [
"MMrTRE",
"AMrTRE"
] | Deformable medical image registration: setting the state of the art with discrete methods |
A challenge of skeleton-based action recognition is the difficulty to classify actions with similar motions and object-related actions. Visual clues from other streams help in that regard. RGB data are sensible to illumination conditions, thus unusable in the dark. To alleviate this issue and still benefit from a visual stream, we propose a modular network (FUSION) combining skeleton and infrared data. A 2D convolutional neural network (CNN) is used as a pose module to extract features from skeleton data. A 3D CNN is used as an infrared module to extract visual cues from videos. Both feature vectors are then concatenated and exploited conjointly using a multilayer perceptron (MLP). Skeleton data also condition the infrared videos, providing a crop around the performing subjects and thus virtually focusing the attention of the infrared module. Ablation studies show that using pre-trained networks on other large scale datasets as our modules and data augmentation yield considerable improvements on the action classification accuracy. The strong contribution of our cropping strategy is also demonstrated. We evaluate our method on the NTU RGB+D dataset, the largest dataset for human action recognition from depth cameras, and report state-of-the-art performances. | [] | [
"Action Classification",
"Action Classification ",
"Action Recognition",
"Data Augmentation",
"Skeleton Based Action Recognition",
"Temporal Action Localization"
] | [] | [
"NTU RGB+D"
] | [
"Accuracy (CS)",
"Accuracy (CV)"
] | Infrared and 3D skeleton feature fusion for RGB-D action recognition |
This paper investigates the principles of embedding learning to tackle the challenging semi-supervised video object segmentation. Different from previous practices that only explore the embedding learning using pixels from foreground object (s), we consider background should be equally treated and thus propose Collaborative video object segmentation by Foreground-Background Integration (CFBI) approach. Our CFBI implicitly imposes the feature embedding from the target foreground object and its corresponding background to be contrastive, promoting the segmentation results accordingly. With the feature embedding from both foreground and background, our CFBI performs the matching process between the reference and the predicted sequence from both pixel and instance levels, making the CFBI be robust to various object scales. We conduct extensive experiments on three popular benchmarks, i.e., DAVIS 2016, DAVIS 2017, and YouTube-VOS. Our CFBI achieves the performance (J$F) of 89.4%, 81.9%, and 81.4%, respectively, outperforming all the other state-of-the-art methods. Code: https://github.com/z-x-yang/CFBI. | [] | [
"Semantic Segmentation",
"Semi-Supervised Video Object Segmentation",
"Video Object Segmentation",
"Video Semantic Segmentation",
"Youtube-VOS"
] | [] | [
"DAVIS 2017 (val)",
"YouTube-VOS",
"DAVIS 2017 (test-dev)",
"DAVIS 2016"
] | [
"Jaccard (Mean)",
"Jaccard (Unseen)",
"F-Measure (Seen)",
"Jaccard (Seen)",
"Overall",
"F-measure (Mean)",
"J&F",
"F-Measure (Unseen)"
] | Collaborative Video Object Segmentation by Foreground-Background Integration |
Toxic online content has become a major issue in today's world due to an
exponential increase in the use of internet by people of different cultures and
educational background. Differentiating hate speech and offensive language is a
key challenge in automatic detection of toxic text content. In this paper, we
propose an approach to automatically classify tweets on Twitter into three
classes: hateful, offensive and clean. Using Twitter dataset, we perform
experiments considering n-grams as features and passing their term
frequency-inverse document frequency (TFIDF) values to multiple machine
learning models. We perform comparative analysis of the models considering
several values of n in n-grams and TFIDF normalization methods. After tuning
the model giving the best results, we achieve 95.6% accuracy upon evaluating it
on test data. We also create a module which serves as an intermediate between
user and Twitter. | [] | [
"Hate Speech Detection"
] | [] | [
"Hate Speech and Offensive Language"
] | [
"Accuracy"
] | Detecting Hate Speech and Offensive Language on Twitter using Machine Learning: An N-gram and TFIDF based Approach |
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