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Video CCAM
Video-CCAM
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Video CCAM
AI video generationAI video editing#Video Understanding#Multilingual Models#Causal Cross-AttentionStandard PicksOpen Source
Overview :
Video-CCAM is a series of flexible video multilingual models (Video-MLLM) developed by the Tencent QQ Multimedia Research Team, aimed at enhancing video-language understanding, particularly suitable for both short and long video analysis. It achieves this through Causal Cross-Attention Masks. Video-CCAM has shown outstanding performance across multiple benchmark tests, especially in MVBench, VideoVista, and MLVU. The source code has been rewritten to streamline the deployment process.
Target Users :
Video-CCAM is designed for researchers and developers who need to analyze and understand video content, particularly in the fields of video language models and multimodal learning. It helps users gain deeper insights into video content, enhancing the accuracy and efficiency of video analysis.
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Use Cases
In the Video-MME benchmark test, Video-CCAM-14B achieved scores of 53.2 (without subtitles) and 57.4 (with subtitles) for 96 frames.
Video-CCAM ranked second and third in evaluations on VideoVista, demonstrating its competitiveness among open-source MLLMs.
Using 16 frames, Video-CCAM-4B and Video-CCAM-9B achieved scores of 57.78 and 60.70 respectively on MVBench.
Features
Exhibits outstanding performance in various video understanding benchmark tests.
Supports the analysis of both short and long videos.
Enhances video-language understanding capabilities using Causal Cross-Attention Mask technology.
Rewritten source code to simplify the deployment process.
Supports Huggingface transformers for inference on NVIDIA GPUs.
Provides detailed tutorials and examples for easy learning and application.
How to Use
1. Visit the GitHub repository page to learn about the basic information and functions of Video-CCAM.
2. Read the README.md file for installation and usage instructions.
3. Follow the tutorial provided in tutorial.ipynb to learn how to utilize Huggingface transformers for model inference on an NVIDIA GPU.
4. Download or clone the source code for local deployment and testing as needed.
5. Utilize the model for video content analysis and understanding, adjusting parameters and configurations based on actual requirements.
6. Engage in community discussions for technical support and best practices.
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