Generalized Open-World Semi-Supervised Object Detection

• URL: http://arxiv.org/abs/2307.15710v2
• Date: Fri, 01 Nov 2024 20:44:02 GMT
• Title: Generalized Open-World Semi-Supervised Object Detection
• Authors: Garvita Allabadi, Ana Lucic, Siddarth Aananth, Tiffany Yang, Yu-Xiong Wang, Vikram Adve,
• Abstract summary: We introduce an ensemble-based OOD Explorer for detection and classification, and an adaptable semi-supervised object detection framework. We demonstrate that our method performs competitively against state-of-the-art OOD detection algorithms and also significantly boosts the semi-supervised learning performance for both ID and OOD classes.
• Score: 22.058195650206944
• License:
• Abstract: Traditional semi-supervised object detection methods assume a fixed set of object classes (in-distribution or ID classes) during training and deployment, which limits performance in real-world scenarios where unseen classes (out-of-distribution or OOD classes) may appear. In such cases, OOD data is often misclassified as ID, thus harming the ID classes accuracy. Open-set methods address this limitation by filtering OOD data to improve ID performance, thereby limiting the learning process to ID classes. We extend this to a more natural open-world setting, where the OOD classes are not only detected but also incorporated into the learning process. Specifically, we explore two key questions: 1) how to accurately detect OOD samples, and, most importantly, 2) how to effectively learn from the OOD samples in a semi-supervised object detection pipeline without compromising ID accuracy. To address this, we introduce an ensemble-based OOD Explorer for detection and classification, and an adaptable semi-supervised object detection framework that integrates both ID and OOD data. Through extensive evaluation on different open-world scenarios, we demonstrate that our method performs competitively against state-of-the-art OOD detection algorithms and also significantly boosts the semi-supervised learning performance for both ID and OOD classes.

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