Reducing Annotation Effort by Identifying and Labeling Contextually Diverse Classes for Semantic Segmentation Under Domain Shift

Abstract

In Active Domain Adaptation (ADA), one uses Active Learning (AL) to select target domain frames to annotate for Domain Adaptation (DA). Thus, ADA creates a continuum of cost-performance trade-off models, with unsupervised, and fully supervised DA techniques at the two ends. We observe that in ADA not all regions of a selected frame contribute equally to a model's performance, and there is a strong correlation between annotating certain hard/unique/novel object/stuff instances, and a model's performance. E.g., road regions in a target dataset may look mostly similar to source domain except for certain curved instances, where annotation may be more useful. Based on the observation, we propose Anchor-based and Augmentation-based ADA techniques, which, given a selected frame, determine certain 'hard' semantic regions to be annotated in that frame, such that the selected regions are complementary and diverse in the context of the current labeled set. The proposed techniques carefully avoid the pitfall of region based AL techniques which try to choose most uncertain regions in a frame, but ends up selecting all edge pixels, and similar annotation cost as the whole frame. We show that our approach achieves 66.6 \miou on \gta->\cityscapes dataset with a budget of 4.7% in comparison to 64.9 \miou by MADA [??]. Our technique can also be used as a decorator for any existing frame-based AL technique. E.g., we report 1.5% performance improvement for CDAL [??] on \cityscapes using our approach.