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arXiv · 2611.06302

Beyond Scene Shortcuts: Action-Centric Video-Text Alignment for Fine-Grained Action Understanding

Zhiliang Zhu·Jiazheng Huang·Dawei Zhao
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Abstract

Fine-grained action understanding requires vision-language models to ground what an agent does, how it acts, and why it acts in spatio-temporal evidence. However, richer supervision can backfire: fine-tuned models may generate detailed action descriptions grounded in static scenes and salient objects rather than motion, a failure we call Environment-Grounded Action Hallucination (EGAH). To mitigate EGAH, we propose Staged Attention Rectification and Reinforcement (STARR), an action-centric video-text alignment framework that breaks scene-action shortcuts through Action-Environment Dissociation and reinforces action-focused grounding with an Attention Consistency Reward within Group Relative Policy Optimization. We further construct FineActionR&R, combining temporally localized captions with hierarchical and temporal-causal reasoning data. Built on Qwen2.5-VL-7B, FineActionVLM-R1 improves average MotionBench accuracy by 3.01 percentage points over its base model and achieves 91.50% on HAU-MCQ-Bench. Qualitative comparisons further indicate more faithful fine-grained action descriptions with fewer environment-induced hallucinations.

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Zhiliang Zhu, Jiazheng Huang, Dawei Zhao. 2026-11-04. Beyond Scene Shortcuts: Action-Centric Video-Text Alignment for Fine-Grained Action Understanding. https://arxiv.org/abs/2611.06302

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