S4OD: Semi-Supervised learning for Single-Stage Object Detection

• URL: http://arxiv.org/abs/2204.04492v1
• Date: Sat, 9 Apr 2022 15:19:37 GMT
• Title: S4OD: Semi-Supervised learning for Single-Stage Object Detection
• Authors: Yueming Zhang, Xingxu Yao, Chao Liu, Feng Chen, Xiaolin Song, Tengfei Xing, Runbo Hu, Hua Chai, Pengfei Xu, and Guoshan Zhang
• Abstract summary: Single-stage detectors suffer from extreme foreground-background class imbalance, while two-stage detectors do not. In semi-supervised object detection, two-stage detectors can deliver remarkable performance by only selecting high-quality pseudo labels.
• Score: 17.44592826610368
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Single-stage detectors suffer from extreme foreground-background class imbalance, while two-stage detectors do not. Therefore, in semi-supervised object detection, two-stage detectors can deliver remarkable performance by only selecting high-quality pseudo labels based on classification scores. However, directly applying this strategy to single-stage detectors would aggravate the class imbalance with fewer positive samples. Thus, single-stage detectors have to consider both quality and quantity of pseudo labels simultaneously. In this paper, we design a dynamic self-adaptive threshold (DSAT) strategy in classification branch, which can automatically select pseudo labels to achieve an optimal trade-off between quality and quantity. Besides, to assess the regression quality of pseudo labels in single-stage detectors, we propose a module to compute the regression uncertainty of boxes based on Non-Maximum Suppression. By leveraging only 10% labeled data from COCO, our method achieves 35.0% AP on anchor-free detector (FCOS) and 32.9% on anchor-based detector (RetinaNet).

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