Single Image Test-Time Adaptation for Segmentation

TL;DR

This paper introduces a method for adapting segmentation models to a single unlabelled test image using self-supervised and adversarial training, significantly improving robustness to domain shifts.

Contribution

It proposes a novel test-time adaptation technique for segmentation that works on a single image without additional data, enhancing model performance.

Findings

Achieved over 3% improvement in segmentation accuracy with adaptation.

Evaluated multiple baselines and introduced adversarial training for mask refinement.

Significant performance gains compared to non-adapted models.

Abstract

Test-Time Adaptation (TTA) methods improve the robustness of deep neural networks to domain shift on a variety of tasks such as image classification or segmentation. This work explores adapting segmentation models to a single unlabelled image with no other data available at test-time. In particular, this work focuses on adaptation by optimizing self-supervised losses at test-time. Multiple baselines based on different principles are evaluated under diverse conditions and a novel adversarial training is introduced for adaptation with mask refinement. Our additions to the baselines result in a 3.51 and 3.28 % increase over non-adapted baselines, without these improvements, the increase would be 1.7 and 2.16 % only.