Pixel Exclusion: Uncertainty-aware Boundary Discovery for Active Cross-Domain Semantic Segmentation

Unsupervised Domain Adaptation (UDA) has been shown to alleviate the heavy annotations for semantic segmentation. Recently, numerous self-training approaches are proposed to address the challenging cross-domain semantic segmentation problem. However, there still exists two open issues: (1) The generated pseudo-labels are inevitably noisy without external supervision. (2) These is a performance gap between UDA models and the fully-supervised model. In this paper, we propose to investigate Active Learning (AL) that selects a small portion of unlabeled pixels (or images) to be annotated, which leads to an impressive performance gain. Specifically, we propose a novel Uncertainty-aware Boundary Discovery (UBD) strategy that selects the uncertain pixels in the boundary areas that contains rich contextual information. Technically, we firstly select the pixels with top entropy values, and then re-select the pixels that are exclusive to their neighbors. We leverage the Kullback-Leibler divergence between one pixel's softmax prediction and its neighbors' to measure its "exclusivity". Extensive experiments show that our approach outperforms previous methods with both pixel-level and image-level label acquisition protocols.