ODDObjects: A Framework for Multiclass Unsupervised Anomaly Detection on Masked Objects

• URL: http://arxiv.org/abs/2104.12300v1
• Date: Mon, 26 Apr 2021 01:13:28 GMT
• Title: ODDObjects: A Framework for Multiclass Unsupervised Anomaly Detection on Masked Objects
• Authors: Ricky Ma (The University of British Columbia)
• Abstract summary: ODDObjects is designed to detect anomalies of various categories using unsupervised autoencoders trained on COCO-style datasets. The framework extends previous work on anomaly detection with autoencoders, comparing state-of-the-art models trained on object recognition datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper presents a novel framework for unsupervised anomaly detection on masked objects called ODDObjects, which stands for Out-of-Distribution Detection on Objects. ODDObjects is designed to detect anomalies of various categories using unsupervised autoencoders trained on COCO-style datasets. The method utilizes autoencoder-based image reconstruction, where high reconstruction error indicates the possibility of an anomaly. The framework extends previous work on anomaly detection with autoencoders, comparing state-of-the-art models trained on object recognition datasets. Various model architectures were compared, and experimental results show that memory-augmented deep convolutional autoencoders perform the best at detecting out-of-distribution objects.

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