# Semi-Supervised Domain Adaptation via Adaptive and Progressive Feature Alignment

Source: [Semi-Supervised Domain Adaptation via Adaptive and Progressive Feature Alignment](<https://www.sota2.com/research/paper/68fa6a221e209131cea263af>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:17:37.192Z

Authors: Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu

Published: 2021-06-05T09:12:50.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2106.02845>)

[PDF](<https://arxiv.org/pdf/2106.02845.pdf>)