PICA: Point-wise Instance and Centroid Alignment Based Few-shot Domain Adaptive Object Detection with Loose Annotations

In this work, we focus on supervised domain adaptation for object detection in few-shot loose annotation setting, where the source images are sufficient and fully labeled but the target images are few-shot and loosely annotated. As annotated objects exist in the target domain, instance level alignment can be utilized to improve the performance. Traditional methods conduct the instance level alignment by semantically aligning the distributions of paired object features with domain adversarial training. Although it is demonstrated that point-wise surrogates of distribution alignment provide a more effective solution in few-shot classification tasks across domains, this point-wise alignment approach has not yet been extended to object detection. In this work, we propose a method that extends the point-wise alignment from classification to object detection. Moreover, in the few-shot loose annotation setting, the background ROIs of target domain suffer from severe label noise problem, which may make the point-wise alignment fail. To this end, we exploit moving average centroids to mitigate the label noise problem of background ROIs. Meanwhile, we exploit point-wise alignment over instances and centroids to tackle the problem of scarcity of labeled target instances. Hence this method is not only robust against label noises of background ROIs but also robust against the scarcity of labeled target objects. Experimental results show that the proposed instance level alignment method brings significant improvement compared with the baseline and is superior to state-of-the-art methods.