VideoDetective: Clue Hunting via both Extrinsic Query and Intrinsic Relevance for Long Video Understanding

2026-03-23 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

VideoDetective is a new framework designed to improve long video understanding for multimodal large language models (MLLMs) by addressing their limited context windows. Existing methods primarily rely on query-based localization, often neglecting the video's intrinsic structure and the varying relevance of different segments. VideoDetective integrates both query-to-segment relevance and inter-segment affinity to identify critical clues in long-video question answering. It constructs a visual-temporal affinity graph from video segments, based on visual similarity and temporal proximity. The framework employs a Hypothesis-Verification-Refinement loop to estimate and propagate relevance scores, guiding the localization of the most crucial segments. This approach has demonstrated significant performance gains, achieving accuracy improvements of up to 7.5% on the VideoMME-long benchmark across various mainstream MLLMs.

Key takeaway

For research scientists developing long video understanding systems, VideoDetective's approach of combining query relevance with intrinsic video structure offers a robust method to overcome context window limitations. You should consider implementing visual-temporal affinity graphs and relevance propagation techniques to significantly improve clue hunting and overall accuracy in MLLM-based applications, potentially yielding gains like the reported 7.5% on VideoMME-long.

Key insights

Integrating extrinsic query relevance with intrinsic video structure enhances long video understanding for MLLMs.

Principles

• Video segments have varying intrinsic relevance.
• Visual-temporal affinity graphs model video structure.
• Relevance propagation improves clue localization.

Method

VideoDetective divides videos into segments, builds a visual-temporal affinity graph, and uses a Hypothesis-Verification-Refinement loop to estimate and propagate relevance scores for critical segment localization.

In practice

• Use visual-temporal graphs for long video analysis.
• Propagate relevance scores to unseen segments.
• Apply to MLLM-based video question answering.

Topics

• Long Video Understanding
• Multimodal Large Language Models
• Video Question Answering
• Visual-Temporal Graphs
• Computer Vision

Best for: Research Scientist, AI Researcher, AI Scientist, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.