| title | VidUEU-Agent: A Data-Curation Agent for Multimodal Understanding, Editing, and Unified Tasks |
|---|---|
| authors | Tengju Ru, Weitong Lian, Zecong Tang, Lingyi Meng, Haoran Li, Zhejun Cui, Yichen Zhu, Hangshuo Cao, Qi Kang, Yechi Liu, Kaixuan Wang, Yu-Jie Yuan, Chunwei Wang, Yu Zhang, Bo Dai |
| date | 2026-11-03 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2611.04418 |
| upvotes | 24 (Hugging Face Daily Papers, 2026-11-05) |
| links | abs pdf hf code |
| cite | |
| abstract | The advancement of multimodal learning is heavily constrained by the scarcity and high annotation cost of high-quality instruction data. While raw videos offer an abundant and dynamic source of visual knowledge, transforming them into precise instruction data remains highly challenging. To bridge this gap, we present VidUEU-Agent, an automated pipeline that synthesizes instruction data from raw videos for multimodal understanding, editing, and unified tasks. VidUEU-Agent follows a four-stage pipeline: it performs global perception to filter low-quality shots, mines keyframes and multimodal signals, constructs metadata and routes the metadata to synthesize instruction samples. More importantly, our agent framework can autonomously iterate the data distribution based on downstream training results, thereby optimizing data quality, balance, and training effectiveness. Scaling this pipeline, we construct VidUEU-100K, a large-scale dataset of 100K training samples spanning multiple domains and tasks, providing a robust foundation for developing comprehensive multimodal capabilities. Extensive experiments demonstrate that fine-tuning baseline models on VidUEU-100K yields significant and consistent performance gains across diverse representative benchmarks, validating the effectiveness of our proposed agent framework and dataset. |
bibtex
@misc{ru2026vidueu,
title={VidUEU-Agent: A Data-Curation Agent for Multimodal Understanding, Editing, and Unified Tasks},
author={Tengju Ru and Weitong Lian and Zecong Tang and Lingyi Meng and Haoran Li and Zhejun Cui and Yichen Zhu and Hangshuo Cao and Qi Kang and Yechi Liu and Kaixuan Wang and Yu-Jie Yuan and Chunwei Wang and Yu Zhang and Bo Dai},
year={2026},
eprint={2611.04418},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2611.04418}
}