VideoForest: Person-Anchored Hierarchical Reasoning for Cross-Video Question Answering

• URL: http://arxiv.org/abs/2508.03039v1
• Date: Tue, 05 Aug 2025 03:33:24 GMT
• Title: VideoForest: Person-Anchored Hierarchical Reasoning for Cross-Video Question Answering
• Authors: Yiran Meng, Junhong Ye, Wei Zhou, Guanghui Yue, Xudong Mao, Ruomei Wang, Baoquan Zhao,
• Abstract summary: Cross-video question answering presents significant challenges beyond traditional single-video understanding.<n>We introduce VideoForest, a novel framework that addresses these challenges through person-anchored hierarchical reasoning.<n>Our approach leverages person-level features as natural bridge points between videos, enabling effective cross-video understanding without requiring end-to-end training.
• Score: 14.039561301034848
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-video question answering presents significant challenges beyond traditional single-video understanding, particularly in establishing meaningful connections across video streams and managing the complexity of multi-source information retrieval. We introduce VideoForest, a novel framework that addresses these challenges through person-anchored hierarchical reasoning. Our approach leverages person-level features as natural bridge points between videos, enabling effective cross-video understanding without requiring end-to-end training. VideoForest integrates three key innovations: 1) a human-anchored feature extraction mechanism that employs ReID and tracking algorithms to establish robust spatiotemporal relationships across multiple video sources; 2) a multi-granularity spanning tree structure that hierarchically organizes visual content around person-level trajectories; and 3) a multi-agent reasoning framework that efficiently traverses this hierarchical structure to answer complex cross-video queries. To evaluate our approach, we develop CrossVideoQA, a comprehensive benchmark dataset specifically designed for person-centric cross-video analysis. Experimental results demonstrate VideoForest's superior performance in cross-video reasoning tasks, achieving 71.93% accuracy in person recognition, 83.75% in behavior analysis, and 51.67% in summarization and reasoning, significantly outperforming existing methods. Our work establishes a new paradigm for cross-video understanding by unifying multiple video streams through person-level features, enabling sophisticated reasoning across distributed visual information while maintaining computational efficiency.

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