[Submitted on 3 Oct 2026]
Title:RADC: Risk-Aware Dual Caching for Vision-Language Test-Time Adaptation
View a PDF of the paper titled RADC: Risk-Aware Dual Caching for Vision-Language Test-Time Adaptation, by Siyu Huang and 3 other authors
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Abstract:Cache-based test-time adaptation (TTA) for vision-language models is often hindered by background bias in global representations and unreliable entropy-based cache admission under representation variations. To address these limitations, we propose RADC, which enhances prototype learning through reliable dual caching. RADC introduces a Semantic Foreground Cache that aggregates category-consistent spatial evidence from CLIP representations, yielding foreground prototypes that complement the global cache while mitigating background interference. To reliably manage both caches, Gaussian Risk Admission models multi-view representations as diagonal Gaussian distributions and jointly considers class separation and feature uncertainty to prioritize reliable cache candidates. RADC integrates zero-shot logits with complementary global- and foreground-cache predictions for robust inference. Extensive experiments on cross-domain and out-of-distribution benchmarks demonstrate consistent state-of-the-art performance.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.06932 [cs.CV]
(or arXiv:2610.06932v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.06932
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Siyu Huang [view email] [v1] Sat, 3 Oct 2026 02:28:11 UTC (12,095 KB)
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