Detect known and unknown objects with semi-supervised learning by auto-encoder ensemble with OWSSD

Detect known and unknown objects with semi-supervised learning by auto-encoder ensemble with OWSSD

Semi-Supervised Object Detection in the Open World

arXiv paper abstract https://arxiv.org/abs/2307.15710

arXiv PDF paper https://arxiv.org/pdf/2307.15710.pdf

Existing approaches for semi-supervised object detection assume a fixed set of classes present in training and unlabeled datasets, i.e., in-distribution (ID) data.

... these techniques significantly degrades when these techniques are deployed in the open-world, due to the fact that the unlabeled and test data may contain objects that were not seen during training, i.e., out-of-distribution (OOD) data.

... propose the Open World Semi-supervised Detection framework (OWSSD) that effectively detects OOD data along with a semi-supervised learning pipeline that learns from both ID and OOD data.

... introduce an ensemble based OOD detector consisting of lightweight auto-encoder networks trained only on ID data.

... demonstrate that ... method performs competitively against state-of-the-art OOD detection algorithms and also significantly boosts the semi-supervised learning performance in open-world scenarios.

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