MacDC: Masking-augmented Collaborative Domain Congregation for Multi-target Domain Adaptation in Semantic Segmentation

Abstract

This paper addresses the challenges in multi-target domain adaptive segmentation which aims at learning a single model that adapts to multiple diverse target domains. Existing methods show limited performance as they only consider the difference in visual appearance (testyle) while ignoring the contextual variations among multiple target domains. In contrast, we propose a novel approach termed Masking-augmented Collaborative Domain Congregation (MacDC) to handle the style gap and contextual gap altogether. The proposed MacDC comprises two key parts: collaborative domain congregation (CDC) and multi-context masking consistency (MCMC). Our CDC handles the style and contextual gaps among target domains by data mixing, which generates image-level and region-level intermediate domains among target domains. To further strengthen contextual alignment, our MCMC adopts a masking-based self-supervised augmentation consistency that enforces the model's understanding of diverse contexts together. Our proposed MacDC directly learns a single model for multi-target domain adaptation without requiring multiple network training and subsequent distillation. Despite its simplicity, MacDC shows efficacy in mitigating the style and contextual gap among multiple target domains and demonstrates superior performance on multi-target domain adaptation for segmentation benchmarks compared to existing state-of-the-art approaches.