Tutorial

Recent advances in anomaly detection

Guansong Pang · Joey Tianyi Zhou · Radu Tudor Ionescu · Yu Tian · Kihyuk Sohn

East 18

Abstract:

The tutorial will present a comprehensive review of recent advances in (deep) anomaly detection on image and video data. Three major AD paradigms will be discussed, including unsupervised/self-supervised approaches (anomaly-free training data), semi-supervised approaches (few-shot training anomaly examples are available), and weakly-supervised approaches (videl-level labels are available for frame-level detection). Additionally, we will also touch on anomaly segementation tasks, focusing on autonomous driving settings. The tutorial will be ended with a panel discussion on AD challenges and opportunities.

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