TP2.L306.5

EFFICIENT ANOMALY DETECTION USING SELF-SUPERVISED MULTI-CUE TASKS

Loic Jezequel, Ngoc-Son Vu, ENSEA, France; Jean Beaudet, Idemia Identity & Security, France; Aymeric Histace, ENSEA, France
Session:
TP2.L306: Anomaly Detection II Lecture
Track:
Image and Video Analysis, Synthesis, and Retrieval
Location:
Room 306
Presentation Time:
Tue, 10 Oct, 17:42 - 18:00 Malaysia Time (UTC +8)
Session Chair:
Peggy Joy Lu, National Center for High-performance Computing
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Session TP2.L306
TP2.L306.1: EXPLORING DIFFUSION MODELS FOR UNSUPERVISED VIDEO ANOMALY DETECTION
Anil Osman Tur, Nicola Dall’Asen, Cigdem Beyan, Elisa Ricci, University of Trento, Italy
TP2.L306.2: ODD: ONE-CLASS ANOMALY DETECTION VIA THE DIFFUSION MODEL
He Wang, Longquan Dai, Jinglin Tong, Nanjing University of Science and Technology, China; Yan Zhai, Communication University of China, China
TP2.L306.3: CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly Detection
Kevin Joo, University of Maryland, United States of America; Khoa Vo, Kashu Yamazaki, Ngan Le, University of Arkansas, United States of America
TP2.L306.4: Flow-based one-class anomaly detection with Multi-frequency Feature fusion
Wei Ma, Shiyong Lan, Weikang Huang, Yitong Ma, Hongyu Yang, Wei Pan, Yilin Zheng, Sichuan University, China
TP2.L306.5: EFFICIENT ANOMALY DETECTION USING SELF-SUPERVISED MULTI-CUE TASKS
Loic Jezequel, Ngoc-Son Vu, ENSEA, France; Jean Beaudet, Idemia Identity & Security, France; Aymeric Histace, ENSEA, France
Contacts