Domain Adaptive Semantic Segmentation using Weak Labels

Publication Date: 8/23/2020

Event: ECCV 2020 – The 16th European Conference on Computer Vision, Glasgow, UK

Reference: https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123540545.pdf

Authors: Sujoy Paul, UC Riverside, NEC Laboratories America, Inc.; Yi-Hsuan Tsai, NEC Laboratories America, Inc.; Samuel Schulter, NEC Laboratories America, Inc.; Amit K. Roy-Chowdhury, UC Riverside, NEC Laboratories America, Inc.; Manmohan Chandraker, NEC Laboratories America, Inc.

Abstract: We propose a novel framework for domain adaptation in semantic segmentation with image-level weak labels in the target domain. The weak labels may be obtained based on a model prediction for unsupervised domain adaptation (UDA), or from a human oracle in a new weakly-supervised domain adaptation (WDA) paradigm for semantic segmentation. Using weak labels is both practical and useful, since (i) collecting image-level target annotations is comparably cheap in WDA and incurs no cost in UDA, and (ii) it opens the opportunity for category-wise domain alignment. Our framework uses weak labels to enable the interplay between feature alignment and pseudo-labeling, improving both in the process of domain adaptation. Specifically, we develop a weak-label classification module to enforce the network to attend to certain categories, and then use such training signals to guide the proposed category-wise alignment method. In experiments, we show considerable improvements with respect to the existing state-of-the-arts in UDA and present a new benchmark in the WDA setting.

Publication Link: https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/896_ECCV_2020_paper.php