Selected work

Representative Papers

FineVAU: A Novel Human-Aligned Benchmark for Fine-Grained Video Anomaly Understanding

Jan 24, 2026

Current evaluation methods for video anomaly understanding (VAU) struggle to accurately assess models’ fine-grained descriptive capabilities regarding anomalous events, often diverging from human perception. This work reframes VAU as a tripartite parsing task—capturing the anomaly’s “What,” the involved entities “Who,” and the spatial context “Where”—and introduces FineW3, a new benchmark dataset, along with FVScore, a human-aligned evaluation metric. FVScore enables the first interpretable, fine-grained assessment of large vision-language models (LVLMs) based on their coverage of critical visual elements. Structured automatic annotations augment manual labeling, and scoring is grounded in key visual components. Human evaluations demonstrate that FVScore significantly outperforms existing metrics. Experiments further reveal that LVLMs underperform on tasks requiring spatiotemporal fine-grained reasoning but excel in scenarios with static or strong visual cues.

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Chain-of-Anomaly Thoughts with Large Vision-Language Models

Dec 23, 2025

Large vision-language models (VLMs) suffer from inherent normalcy bias in video surveillance, hindering reliable detection of anomalous behaviors such as criminal acts; moreover, their reasoning lacks inductive bias toward anomalies, leading to systematic false negatives. To address this, we propose CoAT—a multi-agent chain-of-thought anomaly reasoning framework—that introduces, for the first time, an explicit inductive crime bias mechanism at the end of the reasoning chain and designs an anomaly-focused classification layer to counteract normalcy bias. CoAT integrates multi-agent collaborative reasoning, chain-of-thought (CoT) expansion, and anomaly-aware vision-language modeling. Experiments demonstrate that CoAT improves anomaly detection F1-score by 11.8 percentage points on low-resolution videos and boosts anomaly classification accuracy by 3.78 percentage points on high-resolution videos. This work is the first to explicitly embed inductive anomaly bias into the VLM reasoning chain, significantly enhancing sensitivity to and discriminative capability for criminal events.

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Recent publications

Latest Papers

FineVAU: A Novel Human-Aligned Benchmark for Fine-Grained Video Anomaly Understanding

Jan 24, 2026

Current evaluation methods for video anomaly understanding (VAU) struggle to accurately assess models’ fine-grained descriptive capabilities regarding anomalous events, often diverging from human perception. This work reframes VAU as a tripartite parsing task—capturing the anomaly’s “What,” the involved entities “Who,” and the spatial context “Where”—and introduces FineW3, a new benchmark dataset, along with FVScore, a human-aligned evaluation metric. FVScore enables the first interpretable, fine-grained assessment of large vision-language models (LVLMs) based on their coverage of critical visual elements. Structured automatic annotations augment manual labeling, and scoring is grounded in key visual components. Human evaluations demonstrate that FVScore significantly outperforms existing metrics. Experiments further reveal that LVLMs underperform on tasks requiring spatiotemporal fine-grained reasoning but excel in scenarios with static or strong visual cues.

0 citationsRead paper

Chain-of-Anomaly Thoughts with Large Vision-Language Models

Dec 23, 2025

Large vision-language models (VLMs) suffer from inherent normalcy bias in video surveillance, hindering reliable detection of anomalous behaviors such as criminal acts; moreover, their reasoning lacks inductive bias toward anomalies, leading to systematic false negatives. To address this, we propose CoAT—a multi-agent chain-of-thought anomaly reasoning framework—that introduces, for the first time, an explicit inductive crime bias mechanism at the end of the reasoning chain and designs an anomaly-focused classification layer to counteract normalcy bias. CoAT integrates multi-agent collaborative reasoning, chain-of-thought (CoT) expansion, and anomaly-aware vision-language modeling. Experiments demonstrate that CoAT improves anomaly detection F1-score by 11.8 percentage points on low-resolution videos and boosts anomaly classification accuracy by 3.78 percentage points on high-resolution videos. This work is the first to explicitly embed inductive anomaly bias into the VLM reasoning chain, significantly enhancing sensitivity to and discriminative capability for criminal events.

0 citationsRead paper