SAMPLING FOR UNSUPERVISED DOMAIN ADAPTIVE OBJECT DETECTION

We explore the problem of extreme class imbalance present when performing fully unsupervised domain adaptation for object detection. The main challenge arises from the fact that images in unconstrained settings are mostly occupied by the background (negative class). Therefore, random sampling will not typically result in a sufficient number of positive samples from the target domain, which is required by domain adaptation methods. Motivated by traditional semi-supervised learning algorithms that aim for better classification using both labeled and unlabeled data, we propose a variation of co-learning technique that automatically constructs a more balanced set of samples from the target domain. We evaluate the effectiveness of our approach using a vehicle detection task in an urban surveillance dataset. Furthermore, we compare the performance of our technique with two other approaches-one based on unbiased learning on multiple training data sets and the other on self-learning.