Robust Unsupervised Domain Adaptation by Retaining Confident Entropy via Edge Concatenation

TL;DR

This paper introduces a novel unsupervised domain adaptation method for semantic segmentation that incorporates edge information into entropy-based adversarial networks, significantly improving boundary delineation and overall accuracy.

Contribution

The paper proposes integrating edge-predicted probabilities into entropy-based adversarial networks and developing a probability-sharing network to enhance domain adaptation performance.

Findings

Achieves superior results on SYNTHIA to Cityscapes benchmark

Outperforms state-of-the-art methods in boundary delineation

Demonstrates robustness across multiple adaptation scenarios

Abstract

The generalization capability of unsupervised domain adaptation can mitigate the need for extensive pixel-level annotations to train semantic segmentation networks by training models on synthetic data as a source with computer-generated annotations. Entropy-based adversarial networks are proposed to improve source domain prediction; however, they disregard significant external information, such as edges, which have the potential to identify and distinguish various objects within an image accurately. To address this issue, we introduce a novel approach to domain adaptation, leveraging the synergy of internal and external information within entropy-based adversarial networks. In this approach, we enrich the discriminator network with edge-predicted probability values within this innovative framework to enhance the clarity of class boundaries. Furthermore, we devised a probability-sharing network that integrates diverse information for more effective segmentation. Incorporating object edges addresses a pivotal aspect of unsupervised domain adaptation that has frequently been neglected in the past -- the precise delineation of object boundaries. Conventional unsupervised domain adaptation methods usually center around aligning feature distributions and may not explicitly model object boundaries. Our approach effectively bridges this gap by offering clear guidance on object boundaries, thereby elevating the quality of domain adaptation. Our approach undergoes rigorous evaluation on the established unsupervised domain adaptation benchmarks, specifically in adapting SYNTHIA $\rightarrow$ Cityscapes and SYNTHIA $\rightarrow$ Mapillary. Experimental results show that the proposed model attains better performance than state-of-the-art methods. The superior performance across different unsupervised domain adaptation scenarios highlights the versatility and robustness of the proposed method.