Universal Domain Adaptation for Semantic Segmentation

TL;DR

This paper introduces UniDA-SS, a novel framework for universal domain adaptation in semantic segmentation that effectively handles unknown category settings without prior knowledge, improving performance over existing methods.

Contribution

The paper proposes UniMAP, a new framework with domain-specific prototypes and image matching, enabling robust semantic segmentation adaptation without prior category knowledge.

Findings

UniMAP outperforms baseline methods on new benchmark.

Domain-specific prototypes improve class separation.

Image matching enhances common class learning.

Abstract

Unsupervised domain adaptation for semantic segmentation (UDA-SS) aims to transfer knowledge from labeled source data to unlabeled target data. However, traditional UDA-SS methods assume that category settings between source and target domains are known, which is unrealistic in real-world scenarios. This leads to performance degradation if private classes exist. To address this limitation, we propose Universal Domain Adaptation for Semantic Segmentation (UniDA-SS), achieving robust adaptation even without prior knowledge of category settings. We define the problem in the UniDA-SS scenario as low confidence scores of common classes in the target domain, which leads to confusion with private classes. To solve this problem, we propose UniMAP: UniDA-SS with Image Matching and Prototype-based Distinction, a novel framework composed of two key components. First, Domain-Specific Prototype-based Distinction (DSPD) divides each class into two domain-specific prototypes, enabling finer separation of domain-specific features and enhancing the identification of common classes across domains. Second, Target-based Image Matching (TIM) selects a source image containing the most common-class pixels based on the target pseudo-label and pairs it in a batch to promote effective learning of common classes. We also introduce a new UniDA-SS benchmark and demonstrate through various experiments that UniMAP significantly outperforms baselines. The code is available at https://github.com/KU-VGI/UniMAP.