InternVideo2.5 Chat 8B
| Property | Value |
|---|---|
| Model Size | 8B parameters |
| Author | OpenGVLab |
| Paper | arXiv:2501.12386 |
| Performance | MVBench: 75.7, LongVideoBench: 60.6, VideoMME: 65.1 |
What is InternVideo2_5_Chat_8B?
InternVideo2.5 is a cutting-edge video multimodal large language model built upon InternVL2.5, specifically designed to enhance the processing and understanding of video content through advanced temporal modeling capabilities. This 8B parameter model represents a significant advancement in video-language understanding, incorporating innovative techniques for processing both short and long-form video content.
Implementation Details
The model utilizes two key technological innovations: Direct Preference Optimization (TPO) for dense vision task annotations and Adaptive Hierarchical Token Compression (HiCo) for efficient spatiotemporal representation. It supports both single-turn and multi-turn conversations about video content, with the ability to process up to 512 frames while maintaining high-quality understanding of temporal relationships.
- Advanced temporal modeling with support for variable frame counts (128-512 frames)
- Efficient video preprocessing with dynamic aspect ratio handling
- Support for both single and multi-turn conversations
- Implementation of flash attention 2 for improved performance
Core Capabilities
- Detailed video description generation
- Fine-grained visual detail recognition
- Long-form temporal structure understanding
- Multi-turn conversation about video content
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its enhanced ability to process long-form video content while maintaining fine-grained detail perception, achieved through the combination of TPO and HiCo techniques. It shows superior performance on multiple benchmarks, including MVBench (75.7) and LongVideoBench (60.6).
Q: What are the recommended use cases?
The model is ideal for applications requiring detailed video content analysis, such as video description generation, visual question answering, and interactive video content discussion. It's particularly effective for scenarios requiring understanding of long-form video content and fine-grained temporal details.
