Towards Universal Video MLLMs with Attribute-Structured and Quality-Verified Instructions

• URL: http://arxiv.org/abs/2602.13013v1
• Date: Fri, 13 Feb 2026 15:20:54 GMT
• Title: Towards Universal Video MLLMs with Attribute-Structured and Quality-Verified Instructions
• Authors: Yunheng Li, Hengrui Zhang, Meng-Hao Guo, Wenzhao Gao, Shaoyong Jia, Shaohui Jiao, Qibin Hou, Ming-Ming Cheng,
• Abstract summary: ASID-1M is an open-source collection of one million structured, fine-grained audiovisual instruction annotations.<n>ASID-Verify is a scalable data curation pipeline for annotation.<n>ASID-Captioner is a video understanding model trained via Supervised Fine-Tuning.
• Score: 74.27249614046309
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Universal video understanding requires modeling fine-grained visual and audio information over time in diverse real-world scenarios. However, the performance of existing models is primarily constrained by video-instruction data that represents complex audiovisual content as single, incomplete descriptions, lacking fine-grained organization and reliable annotation. To address this, we introduce: (i) ASID-1M, an open-source collection of one million structured, fine-grained audiovisual instruction annotations with single- and multi-attribute supervision; (ii) ASID-Verify, a scalable data curation pipeline for annotation, with automatic verification and refinement that enforces semantic and temporal consistency between descriptions and the corresponding audiovisual content; and (iii) ASID-Captioner, a video understanding model trained via Supervised Fine-Tuning (SFT) on the ASID-1M. Experiments across seven benchmarks covering audiovisual captioning, attribute-wise captioning, caption-based QA, and caption-based temporal grounding show that ASID-Captioner improves fine-grained caption quality while reducing hallucinations and improving instruction following. It achieves state-of-the-art performance among open-source models and is competitive with Gemini-3-Pro.

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