AIbase
EN
Home
All Categories
AI video generation
Videollama2 7B 16F Base
VideoLLaMA2-7B-16F-Base
V
Videollama2 7B 16F Base
AI video generationAI video editing#Video Question Answering#Video Subtitling#Multi-Modal#TransformerStandard PicksOpen Source
Overview :
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.
Target Users :
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, and video question-answer systems.
Total Visits： 29.7M
Top Region： US(17.94%)
Website Views ： 54.4K
Use Cases
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.
Features
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.
How to Use
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, such as temperature, maximum new tokens, etc.
5. Run the model for inference to obtain video question answering or subtitling generation results.
6. Analyze and evaluate the model output, and adjust model parameters or conduct further training as needed.
Featured AI Tools
Sora
Sora
AI video generation
17.0M
Animate Anyone
Animate Anyone
Animate Anyone aims to generate character videos from static images driven by signals. Leveraging the power of diffusion models, we propose a novel framework tailored for character animation. To maintain consistency of complex appearance features present in the reference image, we design ReferenceNet to merge detailed features via spatial attention. To ensure controllability and continuity, we introduce an efficient pose guidance module to direct character movements and adopt an effective temporal modeling approach to ensure smooth cross-frame transitions between video frames. By extending the training data, our method can animate any character, achieving superior results in character animation compared to other image-to-video approaches. Moreover, we evaluate our method on benchmarks for fashion video and human dance synthesis, achieving state-of-the-art results.
AI video generation
11.4M
AIbase
Empowering the Future, Your AI Solution Knowledge Base
English简体中文繁體中文にほんご
© 2025AIbase