Domain adaptation with temporal ensembling to local attention region search for object detection

Object detection relies heavily on supervised learning, which requires labeled data for training. However, manual labeling often cannot keep pace with the speed of data collection, and models trained on one dataset may not generalize well to new datasets with different characteristics, leading to domain shift issues. Domain adaptation addresses this problem by leveraging labeled data from a source domain and unlabeled data from a target domain to improve performance on the target domain. Limited by the existing domain adaption architecture, the object detection accuracy in the target domain has much room for improvement. In addition, the global search of feature maps costs too much computation. All these problems make it difficult for domain adaptive object detection to be directly applied to tasks such as medical imaging. To this end, this article proposes two architectures: Region-based Object Detection with Domain Adaptation and Temporal Ensembling (DATE) and Local Attention Region Search Algorithm (LARSA). DATE combines domain adaptation and temporal ensembling to enhance feature alignment between domains. At the same time, LARSA employs an attention mechanism to efficiently search for regions of interest and decide when to terminate the search early. Experiments on various datasets demonstrate the effectiveness of the proposed approaches in improving object detection performance under domain shift and reducing computational cost. The proposed framework has the potential to further promote the application of object detection in the field of medical imaging.