SRVAU-R1: Enhancing Video Anomaly Understanding via Reflection-Aware Learning

• URL: http://arxiv.org/abs/2602.01004v1
• Date: Sun, 01 Feb 2026 03:57:45 GMT
• Title: SRVAU-R1: Enhancing Video Anomaly Understanding via Reflection-Aware Learning
• Authors: Zihao Zhao, Shengting Cao, Muchao Ye,
• Abstract summary: Self-Reflection-Enhanced Reasoning for Video Anomaly Understanding (SRVAU-R1) is a reflection-aware learning framework that incorporates reflection in MLLM reasoning.<n>SRVAU-R1 consistently outperforms existing methods, achieving significant improvements in both temporal anomaly localization accuracy and reasoning quality.
• Score: 7.652418192167207
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-modal large language models (MLLMs) have demonstrated significant progress in reasoning capabilities and shown promising effectiveness in video anomaly understanding (VAU) tasks. However, existing MLLM-based approaches remain largely focused on surface-level descriptions of anomalies, lacking deep reasoning over abnormal behaviors like explicit self-reflection and self-correction. To address that, we propose Self-Reflection-Enhanced Reasoning for Video Anomaly Understanding (SRVAU-R1), a reflection-aware learning framework that incorporates reflection in MLLM reasoning. Specifically, SRVAU-R1 introduces the first reflection-oriented Chain-of-Thought dataset tailored for VAU, providing structured supervision with initial reasoning, self-reflection, and revised reasoning. Based on that, it includes a novel reflection-aware learning paradigm with supervised fine-tuning and reinforcement fine-tuning to enhance multi-modal reasoning for VAU. Extensive experiments on multiple video anomaly benchmarks demonstrate that SRVAU-R1 consistently outperforms existing methods, achieving significant improvements in both temporal anomaly localization accuracy and reasoning quality.

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