#5748. A semantically driven self-supervised algorithm for detecting anomalies in image sets

July 2026publication date
Proposal available till 11-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Signal Processing;
Software;
Computer Vision and Pattern Recognition;
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Abstract:
Humans can readily detect when an image does not belong in a set by comparing semantic information between images to derive meaning and relationships from the colors, shapes, sizes, locations, and textures within them. Current self-supervised anomaly detection algorithms in computer vision do not possess this ability. Most algorithms learn to detect anomalies through a training objective that only optimizes for machine-level features and do not evaluate semantic features. To fill this gap, we propose a novel self-supervised algorithm that detects anomalies by learning and modeling semantic information within a set of images. This is accomplished by first training our algorithm to be sensitive or invariant to targeted semantic information, and then modeling the semantic relationships learned so that we can detect anomalies by measuring how far images deviate from the model.
Keywords:
Anomaly detection; Computer vision; Dimensionality reduction; Feature extraction; Land use classification; Multivariate statistics; Pretext learning; Remote sensing; Representation learning; Self-supervised learning

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