Open-set semi-supervised object detection (OSSOD) leverages practical open-set unlabeled datasets with out-of-distribution (OOD) instances for semi-supervised object detection (SSOD). The main challenge in OSSOD is distinguishing and filtering the OOD instances (i.e., outliers) from in-distribution (ID) instances during pseudo-labeling. The only OSSOD work employs an additional offline OOD detection network trained solely with labeled data for solving this problem. However, the limited training data restricts the potential for improvement. Meanwhile, the offline strategy results in low efficiency. To alleviate these issues, this paper proposes an end-to-end online OSSOD framework that improves performance and efficiency: 1) We propose a semi-supervised outlier filtering method that more effectively filters the OOD instances by using both labeled and unlabeled data. 2) We propose a threshold-free Dual Competing OOD head that further improves the performance by suppressing the mispredictions during semi-supervised outlier filtering. 3) Our proposed method is an online end-to-end trainable OSSOD framework. Experimental results show that our method achieves state-of-the-art performance on several OSSOD benchmarks compared to existing methods. Moreover, additional experiments show that our method can be easily applied to different SSOD frameworks.

翻译：开放集半监督目标检测（OSSOD）利用包含分布外（OOD）实例的实际开放集未标注数据集进行半监督目标检测（SSOD）。OSSOD的主要挑战在于伪标签生成过程中区分并过滤分布外实例（即离群点）与分布内（ID）实例。现有唯一的OSSOD工作采用仅由标注数据训练的外部离线OOD检测网络来解决该问题。然而，有限的训练数据限制了其改进潜力，同时离线策略导致效率低下。为缓解这些问题，本文提出一种端到端的在线OSSOD框架，可在提升性能与效率的同时：1）提出一种半监督离群点过滤方法，通过联合使用标注与未标注数据更有效地过滤OOD实例；2）提出一种无阈值的双竞争OOD头部结构，通过抑制半监督离群点过滤过程中的误预测进一步提升性能；3）所提方法为在线端到端可训练的OSSOD框架。实验结果表明，与现有方法相比，本方法在多个OSSOD基准测试中达到了最先进的性能。此外，附加实验证明本方法可轻松应用于不同的SSOD框架。