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 - DAMO-NLP-SG Video-LLaMA: [EMNLP 2023 Demo] Video-LLaMA: An . . . Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding This is the repo for the Video-LLaMA project, which is working on empowering large language models with video and audio understanding capabilities
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
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
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
Troubleshoot YouTube video errors - Google Help Run an Internet speed test to make sure that your Internet can support the selected video resolution Using multiple devices on the same network may reduce the speed that your device gets You can also change the quality of your video to improve your experience Check the YouTube video's resolution and the recommended speed needed to play the video The table below shows the approximate speeds
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
Video-T1: Test-Time Scaling for Video Generation - GitHub Video-T1: We present the generative effects and performance improvements of video generation under test-time scaling (TTS) settings The videos generated with TTS are of higher quality and more consistent with the prompt than those generated without TTS