UC Riverside

In most domain adaptation methods, concurrent access to source and target data isneeded. In many real-life problem statements, it’s highly likely that the access to the source data might not be possible during the adaptation process. Source-free domain adaptation methods, while very necessary, usually have significant performance gap when compared with those that assume access to the source data. To tackle this, we introduce a source free domain adaptation strategy for the semantic segmentation problem that internally uses active learning on the pixel level predictions (i.e., pseudo labels) to obtain a framework that can generalize well between the source and the target domain. Given a small budget for labeling, we select the pixels needed to be sent to an oracle for labeling. These labeled pixels are then used to boost performance of the the overall system. Our method does not assume any source data or any prior labels available on the target data. We observe that even without the source data and only by labeling a small percentage of pixels using active learning, we can still reach comparable performance with many standard unsupervised domain adaptive semantic segmentation methods.We also show a brief example application of domain adaptation on the environmental scenario