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
VidUEU-Agent automates instruction data synthesis from raw videos via a multi-stage pipeline with autonomous iteration, yielding a 100K-sample dataset that improves multimodal model performance.
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…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
VidUEU-Agent: A Data-Curation Agent for Multimodal Understanding, Editing, and Unified Tasks was first seen on Hugging Face · Daily papers on Nov 4, 2026. Since then it has appeared in 15 checks on 2 different days. Its best position was #7 of 30, reached on Nov 5, 07:28 UTC. It dropped off the list after Nov 5, 11:28 UTC.
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Not at the moment. It was last on the list on Nov 5, 11:28 UTC, after 2 days of trending.
#7 on Hugging Face · Daily papers, on Nov 5, 07:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 4, 2026 and has been on the list on 2 days (15 checks) since.