Contrastive-Domain Mean Teacher for Domain Adaptive Object Detection

Most semi-supervised domain object detection (SDAOD) methods are based on the mean-teacher framework. This framework primarily utilizes object-level features provided by pseudo-labels. However, the pseudo-labels generated by the Teacher model often contain notable noise, which limits the detector’s performance. Unlike pseudo-labels, domain labels are more precise and can offer accurate domain-level features. Motivated by this, we incorporate domain-level features into contrastive learning by designing different label assignment strategies and thus propose Contrastive-Domain Mean Teacher (CDMT) for SDAOD. Specifically, domain-level features include both inter-domain and intra-domain features. For inter-domain features, our strategy regards samples with the same domain label as positive pairs, enabling contrastive learning to extract global feature representations. While, intra-domain features from the same image are treated as positive pairs, which helps contrastive learning to extract fine-grained feature representations. Thorough experiments demonstrate that CDMT achieves state-of-the-art performance on Foggy Cityscapes and Clipart combined with recent Mean Teacher framework methods. Notably, for more challenging foggiest images (’0.02’ split) based on the Probabilistic Teacher (PT) baseline, CDMT outperforms the previously best CMT by 4.1% on mAP, which shows its priority on cross-domain detection tasks.