copy and paste this google map to your website or blog!
Press copy button and paste into your blog or website.
(Please switch to 'HTML' mode when posting into your blog. Examples: WordPress Example, Blogger Example)
Input Form:Deal with this potential dealer,buyer,seller,supplier,manufacturer,exporter,importer
(Any information to deal,buy, sell, quote for products or service)
DepthAnything Video-Depth-Anything - GitHub 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 accuracy
【EMNLP 2024 】Video-LLaVA: Learning United Visual . . . - 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
GitHub - MME-Benchmarks Video-MME: [CVPR 2025] Video-MME: The First . . . 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
Video-R1: Reinforcing Video Reasoning in MLLMs - GitHub Video-R1 significantly outperforms previous models across most benchmarks Notably, on VSI-Bench, which focuses on spatial reasoning in videos, Video-R1-7B achieves a new state-of-the-art accuracy of 35 8%, 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
Wan: Open and Advanced Large-Scale Video Generative Models Wan: Open and Advanced Large-Scale Video Generative Models In this repository, we present Wan2 1, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation Wan2 1 offers these key features:
VideoLLM-online: Online Video Large Language Model for Streaming Video Online Video Streaming: Unlike previous models that serve as offline mode (querying responding to a full video), our model supports online interaction within a video stream It can proactively update responses during a stream, such as recording activity changes or helping with the next steps in real time
Generate Video Overviews in NotebookLM - Google Help Video Overviews, including voices and visuals, are AI-generated and may contain inaccuracies or audio glitches NotebookLM may take a while to generate the Video Overview, feel free to come back to your notebook later
Troubleshoot YouTube video errors - Google Help 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
Wan: Open and Advanced Large-Scale Video Generative Models Wan: Open and Advanced Large-Scale Video Generative Models We are excited to introduce Wan2 2, a major upgrade to our foundational video models With Wan2 2, we have focused on incorporating the following innovations: 👍 Effective MoE Architecture: Wan2 2 introduces a Mixture-of-Experts (MoE) architecture into video diffusion models By separating the denoising process cross timesteps with