Class-unbalanced domain adaptation for object detection via dynamic weighting mechanism

The state-of-the-art object detection frameworks often suffer from a performance decline when the feature distribution differences between the source (training) domain and the target (testing) domain are existed. To alleviate this problem, recent works proposed various domain adaptation methods to improve object detection frameworks. Existing methods only consider the feature discrepancy between the source and target domains and ignores unbalanced in class space under cross-domain settings, which can lead to serious negative transfer problems. To address this issue, we propose a novel domain adaptation for object detection to reduce class space discrepancy between domains. Specifically, the weighted mechanism is used to increase the weight of public categories between domains to promote positive transfer and reduce the weight of non-public categories to retard the impact of negative transfer. Moreover, the model reduces domain feature distribution discrepancy by adding domain classifiers and employing adversarial training methods. The results of our experiments on several datasets demonstrate that our model can effectively solve the problem of performance degradation caused by the discrepancy in class space and significantly improve the detection accuracy in each domain.