Semi-Supervised Object Detection in the Open World

• URL: http://arxiv.org/abs/2307.15710v1
• Date: Fri, 28 Jul 2023 17:59:03 GMT
• Title: Semi-Supervised Object Detection in the Open World
• Authors: Garvita Allabadi, Ana Lucic, Peter Pao-Huang, Yu-Xiong Wang and Vikram Adve
• Abstract summary: We introduce an ensemble based OOD detector consisting of lightweight auto-encoder networks trained only on ID data. Our method performs competitively against state-of-the-art OOD detection algorithms and also significantly boosts the semi-supervised learning performance in open-world scenarios.
• Score: 16.274397329511192
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. The performance of 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. The two key questions that we explore in this paper are: can we detect these OOD samples and if so, can we learn from them? With these considerations in mind, we 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. We introduce an ensemble based OOD detector consisting of lightweight auto-encoder networks trained only on ID data. Through extensive evalulation, we demonstrate that our 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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