Yiyan Zhu — accepted AI-conference papers

2026

Alert-CLIP: Abnormality-aware Latent-Enhanced Representation Tuning of CLIP for Video Anomaly Detection

Yiyan Zhu, Menghao Zhang, Haifeng Sun, Pengfei Ren, Xianao Chu, Chenye Xu +4

CVPR 2026

With the rise of pre-trained vision-language models such as CLIP, performing video anomaly detection (VAD) through cross-modal reasoning has become an emerging trend. However, we observe that CLIP still suffers from weak abnormality awareness: normal and abnormal descriptions are highly entangled in

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2026

Fine-VAD: Towards Fine-Grained Video Anomaly Detection via Progressive Cross-Granularity Learning

Menghao Zhang, Yiyan Zhu, Pengfei Ren, Haifeng Sun, Qi Qi, Zirui Zhuang +4

CVPR 2026

In this paper, we explore video anomaly detection (VAD) from a fine-grained perspective, which aims not only to detect anomalous events but also to identify their specific categories. Due to the limited number of examples per category, existing methods either fail to handle intra-class variation acr

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