Enhancing cross-domain detection: adaptive class-aware contrastive
  transformer
Ziru Zeng Yue Ding Hongtao Lu
Recently,the detection transformer has gained substantial attention for its
inherent minimal post-processing requirement.However,this paradigm relies on
abundant training data,yet in the context of the cross-domain
adaptation,insufficient labels in the target domain exacerbate issues of class
imbalance and model performance degradation.To address these challenges, we
propose a novel class-aware cross domain detection transformer based on the
adversarial learning and mean-teacher framework.First,considering the
inconsistencies between the classification and regression tasks,we introduce an
IoU-aware prediction branch and exploit the consistency of classification and
location scores to filter and reweight pseudo labels.Second, we devise a
dynamic category threshold refinement to adaptively manage model
confidence.Third,to alleviate the class imbalance,an instance-level class-aware
contrastive learning module is presented to encourage the generation of
discriminative features for each class,particularly benefiting minority
classes.Experimental results across diverse domain-adaptive scenarios validate
our method's effectiveness in improving performance and alleviating class
imbalance issues,which outperforms the state-of-the-art transformer based
methods.