Active Domain Adaptation with False Negative Prediction for Object Detection

Abstract

Domain adaptation adapts models to various scenes with different appearances. In this field, active domain adaptation is crucial in effectively sampling a limited number of data in the target domain. We propose an active domain adaptation method for object detection, focusing on quantifying the undetectability of objects. Existing methods for active sampling encounter challenges in considering undetected objects while estimating the uncertainty of model predictions. Our proposed active sampling strategy addresses this issue using an active learning approach that simultaneously accounts for uncertainty and undetectability. Our newly proposed False Negative Prediction Module evaluates the undetectability of images containing undetected objects, enabling more informed active sampling. This approach considers previously overlooked undetected objects, thereby reducing false negative errors. Moreover, using unlabeled data, our proposed method utilizes uncertainty-guided pseudo-labeling to enhance domain adaptation further. Extensive experiments demonstrate that the performance of our proposed method closely rivals that of fully supervised learning while requiring only a fraction of the labeling efforts needed for the latter.