Chain-of-Glimpse: Search-Guided Progressive Object-Grounded Reasoning for Video Understanding
This work addresses the problem of video understanding for AI systems by providing a method that explicitly grounds reasoning in visual objects, improving robustness and generalization over object-agnostic approaches.
Chain-of-Glimpse introduces a search-guided progressive object-grounded reasoning framework for video understanding that anchors each reasoning step to specific visual evidence regions, achieving consistent performance gains across multiple benchmarks including NExTQA, Video-Holmes, CG-Bench Reasoning, and VRBench.
Video understanding requires identifying and reasoning over semantically discriminative visual objects across frames, yet existing object-agnostic solutions struggle to effectively handle substantial object variations over time. To address this, we introduce Chain-of-Glimpse, a search-guided progressive object-grounded reasoning framework that explicitly anchors each reasoning step to specific visual evidence regions, enabling compositional and multi-step decision-making. Formally, Chain-of-Glimpse formulates video reasoning as a step-by-step process that incrementally builds spatially grounded traces around task-relevant visual objects, thereby mitigating over-reliance on saliency-driven cues. Specifically, Chain-of-Glimpse features a search-guided controller, optimized via reinforcement learning with a format reward that significantly incentivizes grounding capability, to iteratively ground visual evidence regions and form reliable reasoning trajectories, yielding accurate and interpretable multi-step decisions. Extensive evaluations on both in domain NExTQA and out-of-domain Video-Holmes, CG-Bench Reasoning, and VRBench benchmarks demonstrate consistent performance gains, robustness and generalization of Chain-of-Glimpse across diverse video reasoning tasks.