Bidirectional Self-Training with Multiple Anisotropic Prototypes for Domain Adaptive Semantic Segmentation

TL;DR

This paper introduces a bidirectional self-training approach with multiple anisotropic prototypes for domain adaptive semantic segmentation, leveraging richer latent space information to improve pseudo labels and class distinction.

Contribution

It proposes a novel method that uses bidirectional prototype generation and multiple anisotropic prototypes modeled by Gaussian Mixture Models for better domain adaptation.

Findings

Achieves state-of-the-art mean IoU on GTA5->Cityscapes and Synthia->Cityscapes tasks.

Significantly improves segmentation accuracy for confusing categories like 'truck' and 'bus'.

Outperforms existing self-training methods in domain adaptive segmentation.

Abstract

A thriving trend for domain adaptive segmentation endeavors to generate the high-quality pseudo labels for target domain and retrain the segmentor on them. Under this self-training paradigm, some competitive methods have sought to the latent-space information, which establishes the feature centroids (a.k.a prototypes) of the semantic classes and determines the pseudo label candidates by their distances from these centroids. In this paper, we argue that the latent space contains more information to be exploited thus taking one step further to capitalize on it. Firstly, instead of merely using the source-domain prototypes to determine the target pseudo labels as most of the traditional methods do, we bidirectionally produce the target-domain prototypes to degrade those source features which might be too hard or disturbed for the adaptation. Secondly, existing attempts simply model each category as a single and isotropic prototype while ignoring the variance of the feature distribution, which could lead to the confusion of similar categories. To cope with this issue, we propose to represent each category with multiple and anisotropic prototypes via Gaussian Mixture Model, in order to fit the de facto distribution of source domain and estimate the likelihood of target samples based on the probability density. We apply our method on GTA5->Cityscapes and Synthia->Cityscapes tasks and achieve 61.2 and 62.8 respectively in terms of mean IoU, substantially outperforming other competitive self-training methods. Noticeably, in some categories which severely suffer from the categorical confusion such as "truck" and "bus", our method achieves 56.4 and 68.8 respectively, which further demonstrates the effectiveness of our design.