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X-MAN: Explaining multiple sources of anomalies in video

Published version
Peer-reviewed

Type

Conference Object

Change log

Authors

Szymanowicz, S 
Charles, J 

Abstract

Our objective is to detect anomalies in video while also automatically explaining the reason behind the detector's response. In a practical sense, explainability is crucial for this task as the required response to an anomaly depends on its nature and severity. However, most leading methods (based on deep neural networks) are not interpretable and hide the decision making process in uninterpretable feature representations. In an effort to tackle this problem we make the following contributions: (1) we show how to build interpretable feature representations suitable for detecting anomalies with state of the art performance, (2) we propose an interpretable probabilistic anomaly detector which can describe the reason behind it's response using high level concepts, (3) we are the first to directly consider object interactions for anomaly detection and (4) we propose a new task of explaining anomalies and release a large dataset for evaluating methods on this task. Our method competes well with the state of the art on public datasets while also providing anomaly explanation based on objects and their interactions.

Description

Keywords

46 Information and Computing Sciences, 4611 Machine Learning

Journal Title

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Conference Name

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Journal ISSN

2160-7508
2160-7516

Volume Title

Publisher

IEEE

Rights

Publisher's own licence