DeepAI AI Chat
Log In Sign Up

CD-TTA: Compound Domain Test-time Adaptation for Semantic Segmentation

by   Junha Song, et al.

Test-time adaptation (TTA) has attracted significant attention due to its practical properties which enable the adaptation of a pre-trained model to a new domain with only target dataset during the inference stage. Prior works on TTA assume that the target dataset comes from the same distribution and thus constitutes a single homogeneous domain. In practice, however, the target domain can contain multiple homogeneous domains which are sufficiently distinctive from each other and those multiple domains might occur cyclically. Our preliminary investigation shows that domain-specific TTA outperforms vanilla TTA treating compound domain (CD) as a single one. However, domain labels are not available for CD, which makes domain-specific TTA not practicable. To this end, we propose an online clustering algorithm for finding pseudo-domain labels to obtain similar benefits as domain-specific configuration and accumulating knowledge of cyclic domains effectively. Moreover, we observe that there is a significant discrepancy in terms of prediction quality among samples, especially in the CD context. This further motivates us to boost its performance with gradient denoising by considering the image-wise similarity with the source distribution. Overall, the key contribution of our work lies in proposing a highly significant new task compound domain test-time adaptation (CD-TTA) on semantic segmentation as well as providing a strong baseline to facilitate future works to benchmark.


page 1

page 4

page 8


TransAdapt: A Transformative Framework for Online Test Time Adaptive Semantic Segmentation

Test-time adaptive (TTA) semantic segmentation adapts a source pre-train...

ADAS: A Direct Adaptation Strategy for Multi-Target Domain Adaptive Semantic Segmentation

In this paper, we present a direct adaptation strategy (ADAS), which aim...

Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentation

In this work, we address the task of unsupervised domain adaptation (UDA...

Feature Alignment and Uniformity for Test Time Adaptation

Test time adaptation (TTA) aims to adapt deep neural networks when recei...

AdapterSoup: Weight Averaging to Improve Generalization of Pretrained Language Models

Pretrained language models (PLMs) are trained on massive corpora, but of...

Towards Understanding GD with Hard and Conjugate Pseudo-labels for Test-Time Adaptation

We consider a setting that a model needs to adapt to a new domain under ...

Bilevel Online Adaptation for Out-of-Domain Human Mesh Reconstruction

This paper considers a new problem of adapting a pre-trained model of hu...