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

Ricky Ma

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arXiv · 2104.12300

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

Ricky Ma

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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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Ricky Ma. 2021-04-26. ODDObjects: A Framework for Multiclass Unsupervised Anomaly Detection on Masked Objects. https://arxiv.org/abs/2104.12300

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