Low-Confidence Samples Mining for Semi-supervised Object Detection
Guandu Liu Fangyuan Zhang Tianxiang Pan Bin Wang
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.