【EMNLP 2024 】Video
ByteDance †Corresponding author This work presents Video Depth Anything based on Depth Anything V2, which can be applied to arbitrarily long videos without compromising quality, consistency, or generalization ability. Compared with other diffusion-based models, it enjoys faster inference speed, fewer parameters, and higher consistent depth. There was an error while loading. Please reload this page. GitHub
Video-LLaVA: Learning United Visual Representation by Alignment Before Projection If you like our project, please give us a star ⭐ on GitHub for latest update. 💡 I also have other video-language projects that may interest you. Open-Sora Plan: Open-Source Large Video Generation Model. There was an error while loading. Please reload this page. 
GitHub
Video-R1 significantly outperforms previous models across most benchmarks. Notably, on VSI-Bench, which focuses on spatial reasoning in videos, Video-RB achieves a new state-of-the-art accuracy of %, surpassing GPT-4o, a proprietary model, while using only 32 frames and 7B parameters. This highlights the necessity of explicit reasoning capability in solving video tasks, and confirms the. There was an error while loading. Please reload this page.
Video
A machine learning-based video super resolution and frame interpolation framework. Est. Hack the Valley II, - k4yt3x/video2x. There was an error while loading. Please reload this page.
Wan
Check the YouTube video’s resolution and the recommended speed needed to play the video. The table below shows the approximate speeds recommended to play each video resolution. There was an error while loading. Please reload this page. DepthAnything/Video
Wan: Open and Advanced Large-Scale Video Generative Models In this repository, we present Wan, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation. Wan offers these key features. . 
Troubleshoot YouTube video errors
We introduce Video-MME, the first-ever full-spectrum, M ulti- M odal E valuation benchmark of MLLMs in Video analysis. It is designed to comprehensively assess the capabilities of MLLMs in processing video data, covering a wide range of visual domains, temporal durations, and data modalities. .
GitHub
Introduced a novel taxonomy for Vid-LLMs based on video representation and LLM functionality. Added a Preliminary chapter, reclassifying video understanding tasks from the perspectives of granularity and language involvement, and enhanced the LLM Background section. .