  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.
