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title: ASID-Caption
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# ASID-Caption
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We build **ASID-Caption**, a data-and-model suite for **fine-grained audiovisual video understanding**.
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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**.
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## What we release
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- **ASID-1M**: a large-scale collection of **attribute-structured** audiovisual instructions with both *single-attribute* and *all-attributes* training formats.
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- **ASID-Verify**: a scalable curation pipeline that generates, ensembles, verifies, and refines annotations to improve semantic and temporal consistency.
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- **ASID-Captioner**: Qwen2.5-Omni-based audiovisual captioning models fine-tuned on ASID-1M.
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## Research interests
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- Video understanding & video captioning
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- Audio-visual learning
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- Multimodal LLMs / instruction tuning
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- Data curation, verification, and quality control
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## Links
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- **Dataset (ASID-1M):** https://huggingface.co/datasets/AudioVisual-Caption/ASID-1M
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- **Models (ASID-Captioner):** https://huggingface.co/AudioVisual-Caption
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## Contact
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For questions, issues, or takedown requests, please open a **Discussion** under the corresponding dataset/model page.
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