  With basic Semi-Supervised Object Detection (SSOD) techniques, one-stage
detectors generally obtain limited promotions compared with two-stage clusters.
We experimentally find that the root lies in two kinds of ambiguities: (1)
Selection ambiguity that selected pseudo labels are less accurate, since
classification scores cannot properly represent the localization quality. (2)
Assignment ambiguity that samples are matched with improper labels in
pseudo-label assignment, as the strategy is misguided by missed objects and
inaccurate pseudo boxes. To tackle these problems, we propose a
Ambiguity-Resistant Semi-supervised Learning (ARSL) for one-stage detectors.
Specifically, to alleviate the selection ambiguity, Joint-Confidence Estimation
(JCE) is proposed to jointly quantifies the classification and localization
quality of pseudo labels. As for the assignment ambiguity, Task-Separation
Assignment (TSA) is introduced to assign labels based on pixel-level
predictions rather than unreliable pseudo boxes. It employs a
"divide-and-conquer" strategy and separately exploits positives for the
classification and localization task, which is more robust to the assignment
ambiguity. Comprehensive experiments demonstrate that ARSL effectively
mitigates the ambiguities and achieves state-of-the-art SSOD performance on MS
COCO and PASCAL VOC. Codes can be found at
https://github.com/PaddlePaddle/PaddleDetection.

Computer Vision and Pattern Recognition