VideoMind: An Omni-Modal Video Dataset with Intent Grounding for Deep-Cognitive Video Understanding

• URL: http://arxiv.org/abs/2507.18552v1
• Date: Thu, 24 Jul 2025 16:19:43 GMT
• Title: VideoMind: An Omni-Modal Video Dataset with Intent Grounding for Deep-Cognitive Video Understanding
• Authors: Baoyao Yang, Wanyun Li, Dixin Chen, Junxiang Chen, Wenbin Yao, Haifeng Lin,
• Abstract summary: VideoMind is a video-centric omni-modal dataset designed for deep video content cognition and enhanced multi-modal feature representation.<n>Every video and its audio is described across three hierarchical layers (factual, abstract, and intent), progressing from surface to depth.<n>VideoMind serves as a powerful benchmark for fine-grained cross-modal alignment and advances fields requiring in-depth video understanding.
• Score: 9.93781167342781
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces VideoMind, a video-centric omni-modal dataset designed for deep video content cognition and enhanced multi-modal feature representation. The dataset comprises 103K video samples (3K reserved for testing), each paired with audio and systematically detailed textual descriptions. Specifically, every video and its audio is described across three hierarchical layers (factual, abstract, and intent), progressing from surface to depth. It contains over 22 million words, averaging ~225 words per sample. VideoMind's key distinction from existing datasets is its provision of intent expressions, which require contextual integration across the entire video and are not directly observable. These deep-cognitive expressions are generated using a Chain-of-Thought (COT) approach, prompting the mLLM through step-by-step reasoning. Each description includes annotations for subject, place, time, event, action, and intent, supporting downstream recognition tasks. Crucially, we establish a gold-standard benchmark with 3,000 manually validated samples for evaluating deep-cognitive video understanding. We design hybrid-cognitive retrieval experiments, scored by multi-level retrieval metrics, to appropriately assess deep video comprehension. Evaluation results for models (e.g., InternVideo, VAST, UMT-L) are released. VideoMind serves as a powerful benchmark for fine-grained cross-modal alignment and advances fields requiring in-depth video understanding, such as emotion and intent recognition. The data is publicly available on GitHub, HuggingFace, and OpenDataLab, https://github.com/cdx-cindy/VideoMind.

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