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Conference Paper Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detection
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Authors
Muhammad Zaigham Zaheer, Jin-ha Lee, Marcella Astrid, Arif Mahmood, Seung-Ik Lee
Issue Date
2020-06
Citation
Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2020, pp.1-4
Language
English
Type
Conference Paper
Abstract
Learning to detect real-world anomalous events using video-level annotations is a difficult task mainly because of the noise present in labels. An anomalous labelled video may actually contain anomaly only in a short duration while the rest of the video can be normal. In the current work, we formulate a weakly supervised anomaly detection method that is trained using only video-level labels. To this end, we propose to utilize binary clustering which helps in mitigating the noise present in the labels of anomalous videos. Our formulation encourages both the main network and the clustering to complement each other in achieving the goal of weakly supervised training. The proposed method yields 78.27% and 84.16% frame-level AUC on UCF-crime and ShanghaiTech datasets respectively, demonstrating its superiority over existing state-of-the-art algorithms.
KSP Keywords
Detection Method, Frame-level, Label noise, Main network, Real-world, anomaly detection, existing state, short duration, state-of-The-Art, weakly supervised training