Semi-Supervised Domain Adaptation via Adaptive and Progressive Feature Alignment

TL;DR

This paper introduces SSDAS, a semi-supervised domain adaptation method for semantic segmentation that leverages a few labeled target samples to improve feature alignment and significantly enhance performance.

Contribution

The paper proposes a novel semi-supervised approach using few labeled target samples as anchors for adaptive and progressive feature alignment in semantic segmentation.

Findings

SSDAS outperforms existing UDA and SSDA baselines.

It can be integrated with UDA methods for improved results.

Extensive experiments validate the effectiveness of SSDAS.

Abstract

Contemporary domain adaptive semantic segmentation aims to address data annotation challenges by assuming that target domains are completely unannotated. However, annotating a few target samples is usually very manageable and worthwhile especially if it improves the adaptation performance substantially. This paper presents SSDAS, a Semi-Supervised Domain Adaptive image Segmentation network that employs a few labeled target samples as anchors for adaptive and progressive feature alignment between labeled source samples and unlabeled target samples. We position the few labeled target samples as references that gauge the similarity between source and target features and guide adaptive inter-domain alignment for learning more similar source features. In addition, we replace the dissimilar source features by high-confidence target features continuously during the iterative training process, which achieves progressive intra-domain alignment between confident and unconfident target features. Extensive experiments show the proposed SSDAS greatly outperforms a number of baselines, i.e., UDA-based semantic segmentation and SSDA-based image classification. In addition, SSDAS is complementary and can be easily incorporated into UDA-based methods with consistent improvements in domain adaptive semantic segmentation.