Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks
Anomaly detection is important in many real-life applications. Recently, self-supervised learning has greatly helped deep anomaly detection by recognizing several geometric transformations. However these methods lack finer features, usually highly depend on the anomaly type, and do not perform well on fine-grained problems. To address these issues, we first introduce in this work three novel and efficient discriminative and generative tasks which have complementary strength: (i) a piece-wise jigsaw puzzle task focuses on structure cues; (ii) a tint rotation recognition is used within each piece, taking into account the colorimetry information; (iii) and a partial re-colorization task considers the image texture. In order to make the re-colorization task more object-oriented than background-oriented, we propose to include the contextual color information of the image border via an attention mechanism. We then present a new out-of-distribution detection function and highlight its better stability compared to existing methods. Along with it, we also experiment different score fusion functions. Finally, we evaluate our method on an extensive protocol composed of various anomaly types, from object anomalies, style anomalies with fine-grained classification to local anomalies with face anti-spoofing datasets. Our model significantly outperforms state-of-the-art with up to 36% relative error improvement on object anomalies and 40% on face anti-spoofing problems.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionColorizationFace Anti-SpoofingObjectObject RecognitionOut-of-Distribution DetectionSelf-Supervised Anomaly DetectionSelf-Supervised LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
CASL: Curvature-Augmented Self-supervised Learning for 3D Anomaly Detection
Deep learning-based 3D anomaly detection methods have demonstrated significant potential in industrial manufacturing. However, many approaches are specifically designed for anomaly detection tasks, which limits their gen…
Point Cloud ClassificationSelf-Supervised LearningRepresentation LearningAnomaly ClassificationSelf-Supervised Representation Learning for Visual Anomaly Detection
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, a…
Anomaly DetectionGeneral Classificationobject-detectionObject Detection+4Anomaly Detection in Video via Self-Supervised and Multi-Task Learning
Anomaly detection in video is a challenging computer vision problem. Due to the lack of anomalous events at training time, anomaly detection requires the design of learning methods without full supervision. In this paper…
Abnormal Event Detection In VideoAnomaly DetectionAnomaly Detection In Surveillance VideosEvent Detection+3Anomaly Detection Requires Better Representations
Anomaly detection seeks to identify unusual phenomena, a central task in science and industry. The task is inherently unsupervised as anomalies are unexpected and unknown during training. Recent advances in self-supervis…
3D Anomaly Detection and SegmentationAnomaly DetectionPositionRepresentation Learning+1UniFormaly: Towards Task-Agnostic Unified Framework for Visual Anomaly Detection
Visual anomaly detection aims to learn normality from normal images, but existing approaches are fragmented across various tasks: defect detection, semantic anomaly detection, multi-class anomaly detection, and anomaly c…
Anomaly DetectionDefect DetectionMulti-class Anomaly DetectionMultiple Instance Learning