In this study, we dive deep into the inconsistency of pseudo targets in
semi-supervised object detection (SSOD). Our core observation is that the
oscillating pseudo-targets undermine the training of an accurate detector. It
injects noise into the student's training, leading to severe overfitting
problems. Therefore, we propose a systematic solution, termed
ConsistentTeacher, to reduce the inconsistency. First, adaptive anchor
assignment~(ASA) substitutes the static IoU-based strategy, which enables the
student network to be resistant to noisy pseudo-bounding boxes. Then we
calibrate the subtask predictions by designing a 3D feature alignment
module~(FAM-3D). It allows each classification feature to adaptively query the
optimal feature vector for the regression task at arbitrary scales and
locations. Lastly, a Gaussian Mixture Model (GMM) dynamically revises the score
threshold of pseudo-bboxes, which stabilizes the number of ground truths at an
early stage and remedies the unreliable supervision signal during training.
ConsistentTeacher provides strong results on a large range of SSOD evaluations.
It achieves 40.0 mAP with ResNet-50 backbone given only 10% of annotated
MS-COCO data, which surpasses previous baselines using pseudo labels by around
3 mAP. When trained on fully annotated MS-COCO with additional unlabeled data,
the performance further increases to 47.7 mAP. Our code is available at
https://github.com/Adamdad/ConsistentTeacher.

本研究探讨了半监督目标检测模型中伪目标的不一致性问题，提出了一种名为 ConsistentTeacher 的系统性解决方案，通过自适应锚定分配、三维特征对齐模块和高斯混合模型等步骤，可以有效减少伪标签带来的噪声和不准确性，提高模型精度。