Adversarial Attacked Teacher for Unsupervised Domain Adaptive Object Detection
This work addresses domain shift challenges in object detection for computer vision applications, offering an incremental improvement over existing teacher-student and adversarial learning methods.
The paper tackles the problem of low-quality pseudo-labels in unsupervised domain adaptive object detection by proposing the Adversarial Attacked Teacher (AAT) framework, which improves pseudo-label quality and achieves a state-of-the-art 52.6 mAP on Clipart1k, surpassing previous methods by 6.7%.
Object detectors encounter challenges in handling domain shifts. Cutting-edge domain adaptive object detection methods use the teacher-student framework and domain adversarial learning to generate domain-invariant pseudo-labels for self-training. However, the pseudo-labels generated by the teacher model tend to be biased towards the majority class and often mistakenly include overconfident false positives and underconfident false negatives. We reveal that pseudo-labels vulnerable to adversarial attacks are more likely to be low-quality. To address this, we propose a simple yet effective framework named Adversarial Attacked Teacher (AAT) to improve the quality of pseudo-labels. Specifically, we apply adversarial attacks to the teacher model, prompting it to generate adversarial pseudo-labels to correct bias, suppress overconfidence, and encourage underconfident proposals. An adaptive pseudo-label regularization is introduced to emphasize the influence of pseudo-labels with high certainty and reduce the negative impacts of uncertain predictions. Moreover, robust minority objects verified by pseudo-label regularization are oversampled to minimize dataset imbalance without introducing false positives. Extensive experiments conducted on various datasets demonstrate that AAT achieves superior performance, reaching 52.6 mAP on Clipart1k, surpassing the previous state-of-the-art by 6.7%.