alibayram/yapay_zeka_turkce_mmlu_model_cevaplari
Viewer
•
Updated
•
6.2k
•
452
•
3
Orkut Murat Yılmaz
orkut
AI & ML interests
Geo Sciences, Free Software
Recent Activity
liked
a dataset
about 1 hour ago
alibayram/yapay_zeka_turkce_mmlu_model_cevaplari
liked
a model
about 1 hour ago
TencentARC/InstantMesh
liked
a model
about 1 hour ago
VAST-AI/MIDI-3D
Organizations
orkut's activity
liked
a
dataset
about 1 hour ago
liked
2
models
about 1 hour ago
liked
a
model
about 18 hours ago
liked
a
Space
3 days ago
liked
a
model
12 days ago
liked
a
Space
12 days ago
liked
a
dataset
about 1 month ago
upvoted
an
article
about 1 month ago
Article
Open-source DeepResearch – Freeing our search agents
•
1.16k
liked
a
model
about 1 month ago
liked
3
models
3 months ago
liked
a
Space
3 months ago
liked
a
model
5 months ago
reacted to
merve's
post with 🔥
8 months ago
Post
2292
We have recently merged Video-LLaVA to transformers! 🤗🎞️
What makes this model different?
Demo: llava-hf/video-llava
Model: LanguageBind/Video-LLaVA-7B-hf
Compared to other models that take image and video input and either project them separately or downsampling video and projecting selected frames, Video-LLaVA is converting images and videos to unified representation and project them using a shared projection layer.
It uses Vicuna 1.5 as the language model and LanguageBind's own encoders that's based on OpenCLIP, these encoders project the modalities to an unified representation before passing to projection layer.
I feel like one of the coolest features of this model is the joint understanding which is also introduced recently with many models
It's a relatively older model but ahead of it's time and works very well! Which means, e.g. you can pass model an image of a cat and a video of a cat and ask questions like whether the cat in the image exists in video or not 🤩
What makes this model different?
Demo: llava-hf/video-llava
Model: LanguageBind/Video-LLaVA-7B-hf
Compared to other models that take image and video input and either project them separately or downsampling video and projecting selected frames, Video-LLaVA is converting images and videos to unified representation and project them using a shared projection layer.
It uses Vicuna 1.5 as the language model and LanguageBind's own encoders that's based on OpenCLIP, these encoders project the modalities to an unified representation before passing to projection layer.
I feel like one of the coolest features of this model is the joint understanding which is also introduced recently with many models
It's a relatively older model but ahead of it's time and works very well! Which means, e.g. you can pass model an image of a cat and a video of a cat and ask questions like whether the cat in the image exists in video or not 🤩
liked
a
model
9 months ago
liked
a
Space
9 months ago
liked
a
model
9 months ago
reacted to
harpreetsahota's
post with 🔥
9 months ago
Post
2237
The Coachella of Computer Vision, CVPR, is right around the corner. In anticipation of the conference, I curated a dataset of the papers.
I'll have a technical blog post out tomorrow doing some analysis on the dataset, but I'm so hyped that I wanted to get it out to the community ASAP.
The dataset consists of the following fields:
- An image of the first page of the paper
-
-
-
-
-
-
-
-
Here's how I created the dataset 👇🏼
Generic code for building this dataset can be found [here](https://github.com/harpreetsahota204/CVPR-2024-Papers).
This dataset was built using the following steps:
- Scrape the CVPR 2024 website for accepted papers
- Use DuckDuckGo to search for a link to the paper's abstract on arXiv
- Use arXiv.py (python wrapper for the arXiv API) to extract the abstract and categories, and download the pdf for each paper
- Use pdf2image to save the image of paper's first page
- Use GPT-4o to extract keywords from the abstract
Voxel51/CVPR_2024_Papers
I'll have a technical blog post out tomorrow doing some analysis on the dataset, but I'm so hyped that I wanted to get it out to the community ASAP.
The dataset consists of the following fields:
- An image of the first page of the paper
-
title: The title of the paper-
authors_list: The list of authors-
abstract: The abstract of the paper-
arxiv_link: Link to the paper on arXiv-
other_link: Link to the project page, if found-
category_name: The primary category this paper according to [arXiv taxonomy](https://arxiv.org/category_taxonomy)-
all_categories: All categories this paper falls into, according to arXiv taxonomy-
keywords: Extracted using GPT-4oHere's how I created the dataset 👇🏼
Generic code for building this dataset can be found [here](https://github.com/harpreetsahota204/CVPR-2024-Papers).
This dataset was built using the following steps:
- Scrape the CVPR 2024 website for accepted papers
- Use DuckDuckGo to search for a link to the paper's abstract on arXiv
- Use arXiv.py (python wrapper for the arXiv API) to extract the abstract and categories, and download the pdf for each paper
- Use pdf2image to save the image of paper's first page
- Use GPT-4o to extract keywords from the abstract
Voxel51/CVPR_2024_Papers