DOME: Learning Transferable Domain Variables from Sparse Supervision for Test-Time Adaptation

• URL: http://arxiv.org/abs/2606.07646v1
• Date: Tue, 02 Jun 2026 03:24:03 GMT
• Title: DOME: Learning Transferable Domain Variables from Sparse Supervision for Test-Time Adaptation
• Abstract summary: Test-time adaptation aims to align a model to shifting test domains using only unlabeled streaming data.<n>Most existing methods implicitly infer a single global domain distribution, ignoring the multidimensional and sample-specific nature of real-world domain shifts.<n>We propose DOME, an effective domain encoder that explicitly models each sample's domain in a zero-shot manner.
• Score: 61.70497741321102
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Test-time adaptation (TTA) aims to align a model to shifting test domains using only unlabeled streaming data. Most existing methods implicitly infer a single global domain distribution, ignoring the multidimensional and sample-specific nature of real-world domain shifts, leading to fragile adaptation. We propose DOME, an effective domain encoder that explicitly models each sample's domain in a zero-shot manner. DOME leverages vision-language pretraining to extract dense, continuous representations, parameterizes domains as distributional variables, and introduces a momentum-updated sparse domain bank for disentangled supervision. By injecting these explicit domain cues into downstream models, even a basic entropy-minimization TTA strategy achieves state-of-the-art performance across ImageNet-C, ImageNet-R, and ImageNet-Sketch, outperforming complex TTA approaches. Our results demonstrate that robust adaptation stems not from intricate adaptation algorithms, but from explicit, structured domain representation.

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