Distribution Optimization Under Gaussian Hypothesis for Domain Adaptive Semantic Segmentation

Abstract

Domain adaptive semantic segmentation aims to transfer a model proficient in dense image classification from a source domain to a target domain. While various transfer methods have been explored in previous studies we argue that the modeling of categories within the model significantly affects its transferability. Building on the Gaussian Hypothesis which posits that each category in the feature space adheres to a multidimensional Gaussian distribution we propose a Class-Aware Variational Inference (CAVI) training method. This approach normalizes features of different categories into distinct multidimensional Gaussian distributions. To further learn domain-independent feature distributions we optimize the feature space using a Gaussian-based alignment strategy and incorporate Gaussian-based contrastive learning. Experimental results demonstrate that our method achieves state-of-the-art performance on the GTAV-to-Cityscapes and Synthia-to-Cityscapes benchmarks.