Low-Confidence Samples Mining for Semi-supervised Object Detection

• URL: http://arxiv.org/abs/2306.16201v1
• Date: Wed, 28 Jun 2023 13:29:06 GMT
• Title: Low-Confidence Samples Mining for Semi-supervised Object Detection
• Authors: Guandu Liu, Fangyuan Zhang, Tianxiang Pan, Bin Wang
• Abstract summary: We propose a novel Low-confidence Samples Mining (LSM) method to utilize low-confidence pseudo-labels efficiently. Our method achieves 3.54% mAP improvement over state-of-the-art methods under 5% labeling ratios.
• Score: 4.414765434786988
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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