Reliable pseudo-labels from unlabeled data play a key role in semi-supervised object detection (SSOD). However, the state-of-the-art SSOD methods all rely on pseudo-labels with high confidence, which ignore valuable pseudo-labels with lower confidence. Additionally, the insufficient excavation for unlabeled data results in an excessively low recall rate thus hurting the network training. In this paper, we propose a novel Low-confidence Samples Mining (LSM) method to utilize low-confidence pseudo-labels efficiently. Specifically, we develop an additional pseudo information mining (PIM) branch on account of low-resolution feature maps to extract reliable large-area instances, the IoUs of which are higher than small-area ones. Owing to the complementary predictions between PIM and the main branch, we further design self-distillation (SD) to compensate for both in a mutually-learning manner. Meanwhile, the extensibility of the above approaches enables our LSM to apply to Faster-RCNN and Deformable-DETR respectively. On the MS-COCO benchmark, our method achieves 3.54% mAP improvement over state-of-the-art methods under 5% labeling ratios.

翻译：可靠的无标签数据伪标签在半监督目标检测（SSOD）中起着关键作用。然而，当前最先进的SSOD方法均依赖于高置信度伪标签，忽略了具有较低置信度的有价值伪标签。此外，对无标签数据的挖掘不足导致召回率过低，进而损害网络训练。本文提出一种新颖的低置信度样本挖掘（LSM）方法，以高效利用低置信度伪标签。具体而言，我们基于低分辨率特征图开发了一个额外的伪信息挖掘（PIM）分支，用于提取可靠的大面积实例——这些实例的交并比（IoU）高于小面积实例。鉴于PIM分支与主分支之间的互补预测，我们进一步设计了自蒸馏（SD）机制，以相互学习的方式对二者进行补偿。同时，上述方法的可扩展性使LSM能够分别应用于Faster-RCNN和Deformable-DETR。在MS-COCO基准测试中，我们的方法在仅使用5%标注比例的条件下，较现有最先进方法实现了3.54%的mAP提升。