VidUEU-Agent: A Data-Curation Agent for Multimodal Understanding, Editing, and Unified Tasks
VidUEU-Agent is an automated pipeline that synthesizes instruction data from raw videos for multimodal understanding, editing, and unified tasks.
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- Primary sourceHugging Face
- Discovery linkHugging Face
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VidUEU-Agent is an automated pipeline that synthesizes instruction data from raw videos for multimodal understanding, editing, and unified tasks. It runs four stages: global perception to filter low-quality shots, keyframe and multimodal signal mining, metadata construction and routing, and instruction synthesis. The agent iterates on data distribution using downstream training results. Using it, the authors built VidUEU-100K, a 100K-sample dataset, and report that fine-tuning baseline models on it yields consistent performance gains across benchmarks.
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A four-stage pipeline filters low-quality shots, mines keyframes, constructs metadata, and routes it to synthesize instruction samples.
Source: Hugging Face ↗Stored source-based brief · key point 1 · AINewsIQ paraphraseThe agent autonomously adjusts data distribution based on downstream training results to improve quality and balance.
Source: Hugging Face ↗Stored source-based brief · key point 2 · AINewsIQ paraphraseVidUEU-100K contains 100K training samples spanning multiple domains and tasks.
Source: Hugging Face ↗Stored source-based brief · key point 3 · AINewsIQ paraphraseFine-tuning baseline models on VidUEU-100K reportedly yields significant and consistent gains across representative benchmarks.
Source: Hugging Face ↗Stored source-based brief · key point 4 · AINewsIQ paraphraseLimits from the stored brief
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Why this may matter
High-quality multimodal instruction data is a major bottleneck. An automated method that turns raw videos into curated, feedback-tuned training data could reduce annotation costs and support more scalable development of unified multimodal models.
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