Self-Supervised Representation Learning for Visual Anomaly Detection
- URL: http://arxiv.org/abs/2006.09654v1
- Date: Wed, 17 Jun 2020 04:37:29 GMT
- Title: Self-Supervised Representation Learning for Visual Anomaly Detection
- Authors: Rabia Ali, Muhammad Umar Karim Khan, Chong Min Kyung
- Abstract summary: We consider the problem of anomaly detection in images videos, and present a new visual anomaly detection technique for videos.
We propose a simple self-supervision approach for learning temporal coherence across video frames without the use of any optical flow information.
This intuitive approach shows superior performance of visual anomaly detection compared to numerous methods for images and videos on UCF101 and ILSVRC2015 video datasets.
- Score: 9.642625267699488
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Self-supervised learning allows for better utilization of unlabelled data.
The feature representation obtained by self-supervision can be used in
downstream tasks such as classification, object detection, segmentation, and
anomaly detection. While classification, object detection, and segmentation
have been investigated with self-supervised learning, anomaly detection needs
more attention. We consider the problem of anomaly detection in images and
videos, and present a new visual anomaly detection technique for videos.
Numerous seminal and state-of-the-art self-supervised methods are evaluated for
anomaly detection on a variety of image datasets. The best performing
image-based self-supervised representation learning method is then used for
video anomaly detection to see the importance of spatial features in visual
anomaly detection in videos. We also propose a simple self-supervision approach
for learning temporal coherence across video frames without the use of any
optical flow information. At its core, our method identifies the frame indices
of a jumbled video sequence allowing it to learn the spatiotemporal features of
the video. This intuitive approach shows superior performance of visual anomaly
detection compared to numerous methods for images and videos on UCF101 and
ILSVRC2015 video datasets.
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