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.

翻译：现有半监督目标检测方法假设训练集与未标注数据集包含固定的类别集合，即分布内（ID）数据。当这些技术部署至开放世界时，由于未标注数据和测试数据可能包含训练中未见过的对象（即分布外OOD数据），其性能会显著下降。本文探讨两个关键问题：能否检测这些OOD样本？若能，是否可从中学习？基于此，我们提出开放世界半监督检测框架（OWSSD），该框架通过半监督学习流程有效检测OOD数据，并同时从ID数据与OOD数据中学习。我们设计了一种基于集成的OOD检测器，由仅在ID数据上训练而成的轻量自编码器网络构成。通过广泛评估，我们证明该方法与现有最先进的OOD检测算法相比具有竞争力，并在开放世界场景下显著提升了半监督学习性能。