Dance Across Shifts: Forward-Facilitation Continual Test-Time Adaptation through Dynamic Style Bridging

2026-05-18

AI Summary

This work addresses the performance degradation in Continual Test-Time Adaptation (CTTA) caused by persistent distribution shifts and unreliable supervisory signals. To this end, it introduces a forward-facilitation paradigm featuring a novel multi-level dynamic style-bridging mechanism that aligns the styles of generated class exemplars with those of incoming test data at the input, statistical, and representation levels, while preserving semantic consistency. This alignment yields high-fidelity supervisory signals that effectively mitigate generation bias and substantially enhance the reliability and adaptability of proxy samples under continual distribution shifts. The proposed method significantly outperforms existing state-of-the-art approaches on standard CTTA benchmarks, achieving consistently stable and sustained performance improvements.

Abstract

Continual Test-Time Adaptation (CTTA) aims to empower perception systems to handle dynamic distribution shifts encountered after deployment. Existing methods predominantly follow a backward-alignment paradigm, which rigidly aligns incoming data with supervisory surrogates derived from the source domain. Consequently, they struggle with unreliable supervision and evolving distribution shifts. To overcome these limitations, we introduce a novel forward-facilitation paradigm through a method termed Dynamic Style Bridging. Prior to deployment, we construct a compact knowledge base of generated class exemplars. During test time, to mitigate inherent generative bias and adapt these proxies to incoming data, we propose a multi-level bridging mechanism. This mechanism dynamically injects the proxies with incoming data styles at the input, statistical, and representation levels, while preserving the original semantics of the proxies. These high-fidelity proxies are then used to provide reliable, on-demand supervisory signals, enabling stable adaptation under continual shifts. Extensive experiments across standard CTTA benchmarks demonstrate that our method achieves consistent and substantial improvements over recent state-of-the-art approaches. Code is available at \href{https://github.com/z1358/DAS}.

Problem

• Continual Test-Time Adaptation
• distribution shifts
• supervisory signals
• dynamic adaptation
• unreliable supervision

Innovation

• Continual Test-Time Adaptation
• Forward-Facilitation
• Dynamic Style Bridging
• Class Exemplars
• Multi-level Bridging