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title: ASID-Caption
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# ASID-Caption

We build **ASID-Caption**, a data-and-model suite for **fine-grained audiovisual video understanding**.

Our goal is to move beyond “one video → one generic caption” by providing **attribute-structured supervision** and **quality-verified annotations**, enabling models to produce **more complete, more controllable, and more temporally consistent** descriptions that cover both **visual content** and **audio cues**.

## What we release

- **ASID-1M**: a large-scale collection of **attribute-structured** audiovisual instructions with both *single-attribute* and *all-attributes* training formats.
- **ASID-Verify**: a scalable curation pipeline that generates, ensembles, verifies, and refines annotations to improve semantic and temporal consistency.
- **ASID-Captioner**: Qwen2.5-Omni-based audiovisual captioning models fine-tuned on ASID-1M.

## Research interests

- Video understanding & video captioning  
- Audio-visual learning  
- Multimodal LLMs / instruction tuning  
- Data curation, verification, and quality control