Open-set semi-supervised object detection (OSSOD) methods aim to utilize
practical unlabeled datasets with out-of-distribution (OOD) instances for
object detection. The main challenge in OSSOD is distinguishing and filtering
the OOD instances from the in-distribution (ID) instances during
pseudo-labeling. The previous method uses an offline OOD detection network
trained only with labeled data for solving this problem. However, the scarcity
of available data limits the potential for improvement. Meanwhile, training
separately leads to low efficiency. To alleviate the above issues, this paper
proposes a novel end-to-end online framework that improves performance and
efficiency by mining more valuable instances from unlabeled data. Specifically,
we first propose a semi-supervised OOD detection strategy to mine valuable ID
and OOD instances in unlabeled datasets for training. Then, we constitute an
online end-to-end trainable OSSOD framework by integrating the OOD detection
head into the object detector, making it jointly trainable with the original
detection task. Our experimental results show that our method works well on
several benchmarks, including the partially labeled COCO dataset with open-set
classes and the fully labeled COCO dataset with the additional large-scale
open-set unlabeled dataset, OpenImages. Compared with previous OSSOD methods,
our approach achieves the best performance on COCO with OpenImages by +0.94
mAP, reaching 44.07 mAP.

本文提出了一种新的端到端在线架构来改进和提高开放集装箱半监督目标检测的性能和效率，并在 COCO 数据集上取得了最佳表现。