Few-Shot Domain Adaptive Object Detection for Microscopic Images

Currently, unsupervised domain adaptive strategies proposed to overcome domain shift, are handicapped by the requirement of large amount of target data. On the other hand medical imaging problems and datasets are often characterized not only by scarcity of labeled and unlabeled data but also class imbalance. Few-shot domain adaptive object detection (FSDAOD) addresses the challenge of adapting object detectors to target domains with limited labeled data. However, existing FSDAOD works struggle with randomly selected target domain images which might not represent the target distribution, resulting in overfitting and poor generalization. We propose a novel FSDAOD strategy for microscopic imaging to tackle high-class imbalance and localization errors due to foreground-background similarity. Our contributions include: a domain adaptive class balancing strategy for few shot scenario and label dependent cross domain feature alignment. Specifically, multi-layer instance-level inter and intra-domain feature alignment is performed by enhancing similarity between the instances of classes regardless of the domain and increasing dissimilarity between instances of different classes. In order to retain the features necessary for localizing and detecting minute texture variations in microscopic objects across the domain, the classification loss was applied at feature-map before the detection head. Extensive experimental results with competitive baselines indicate the effectiveness of our proposed approach, achieving state-of-the-art results on two public microscopic datasets, M5 [12] and Raabin-WBC [10]. Our method outperformed both datasets, increasing average mAP@50 by 8.3 points and 14.6 points, respectively. The project page is available here ( https://im.itu.edu.pk/few-shot-DAODMI/ ).