VideoLLaMA2-7B-16F-Base

A large video language model used for visual question answering and video subtitling generation.

CommonProductVideoVideo Question AnsweringVideo Subtitling

VideoLLaMA2-7B-16F-Base is a large video language model developed by the DAMO-NLP-SG team, focusing on Visual Question Answering (VQA) and video subtitling generation. Combining advanced space-time modeling and audio understanding capabilities, it provides strong support for multi-modal video content analysis. It demonstrates excellent performance in visual question answering and video subtitling generation tasks, capable of handling complex video content and generating accurate descriptions and answers.

Supports both multiple-choice and open-ended video question answering tasks.
Can provide detailed descriptions and analyses of video content.
Integrates advanced Transformer architecture
enhancing the model's understanding and generation capabilities.
Supports multi-modal input
including video and image.
Provides pre-trained models and training code for easy use and further training by researchers and developers.
The model has been trained and evaluated on multiple datasets
demonstrating good generalization ability.

VideoLLaMA2-7B-16F-Base is suitable for researchers
developers
and enterprises that need to process and analyze video content. For example
this model can provide efficient and accurate solutions in areas such as video content analysis
automatic video subtitling generation

Researchers use the VideoLLaMA2-7B-16F-Base model for sentiment analysis of video content.
Developers integrate the model into video question-answering applications
providing an interactive question-and-answer experience for users.
Enterprises utilize the model to automatically generate descriptions and subtitles for video content
improving content production efficiency.

1. Visit the VideoLLaMA2-7B-16F-Base model page to learn about the model's basic information and features.
2. Download or load the pre-trained model and prepare the required video or image data.
3. Based on the specific task
write or use the provided code templates to call the model and process data.
4. Set model parameters

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Monthly Visits

20899836

Bounce Rate

46.04%

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5.2

Visit Duration

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VideoLLaMA2-7B-16F-Base Visit Trend

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VideoLLaMA2-7B-16F-Base

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VideoLLaMA2-7B-16F-Base Visit Trend

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