activity
20222026
most citedHolmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLM

3 citations · 10 across the 9 of their papers we have counts for

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cs.CV2026

Beyond Hazard Resemblance: Contrastive Event Adjudication for Training-Free Video Anomaly Detection

Wenti Yin, Xiang Wang, Huaxin Zhang +4

Video anomaly detection (VAD) aims to identify and temporally localize abnormal events in videos. Supervised methods learn anomaly decision boundaries from target-domain annotation…

cs.CV2025

Learning to Tell Apart: Weakly Supervised Video Anomaly Detection via Disentangled Semantic Alignment

Wenti Yin, Huaxin Zhang, Xiang Wang +7

Recent advancements in weakly-supervised video anomaly detection have achieved remarkable performance by applying the multiple instance learning paradigm based on multimodal founda…

cs.CV2024★ 1 cited

Holmes-VAU: Towards Long-term Video Anomaly Understanding at Any Granularity

Huaxin Zhang, Xiaohao Xu, Xiang Wang +6

How can we enable models to comprehend video anomalies occurring over varying temporal scales and contexts? Traditional Video Anomaly Understanding (VAU) methods focus on frame-lev…

cs.CV2024

Improving Multi-modal Large Language Model through Boosting Vision Capabilities

Yanpeng Sun, Huaxin Zhang, Qiang Chen +5

We focus on improving the visual understanding capability for boosting the vision-language models. We propose \textbf{Arcana}, a multiModal language model, which introduces two cru…

cs.CV2024

Cross-video Identity Correlating for Person Re-identification Pre-training

Jialong Zuo, Ying Nie, Hanyu Zhou +5

Recent researches have proven that pre-training on large-scale person images extracted from internet videos is an effective way in learning better representations for person re-ide…

cs.CV2024★ 3 cited

Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLM

Huaxin Zhang, Xiaohao Xu, Xiang Wang +6

Towards open-ended Video Anomaly Detection (VAD), existing methods often exhibit biased detection when faced with challenging or unseen events and lack interpretability. To address…

cs.CV2024★ 2 cited

GlanceVAD: Exploring Glance Supervision for Label-efficient Video Anomaly Detection

Huaxin Zhang, Xiang Wang, Xiaohao Xu +6

In recent years, video anomaly detection has been extensively investigated in both unsupervised and weakly supervised settings to alleviate costly temporal labeling. Despite signif…

cs.CV2023★ 1 cited

HR-Pro: Point-supervised Temporal Action Localization via Hierarchical Reliability Propagation

Huaxin Zhang, Xiang Wang, Xiaohao Xu +3

Point-supervised Temporal Action Localization (PSTAL) is an emerging research direction for label-efficient learning. However, current methods mainly focus on optimizing the networ…

cs.CV2022★ 3 cited

Context-aware Proposal Network for Temporal Action Detection

Xiang Wang, Huaxin Zhang, Shiwei Zhang +3

This technical report presents our first place winning solution for temporal action detection task in CVPR-2022 AcitivityNet Challenge. The task aims to localize temporal boundarie…