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RADC: Risk-Aware Dual Caching for Vision-Language Test-Time Adaptation
Author1, Author2, Author3, Author4, Author5
cachingtest-time adaptationvision-languageprototype learning
2610.06932
Builder Relevance
1h ago80%
Abstract
RADC enhances prototype learning through reliable dual caching to improve test-time adaptation in vision-language models.
Reality Card
Core Claim
RADC achieves state-of-the-art performance in cross-domain and out-of-distribution benchmarks by effectively managing dual caches to mitigate background interference.
Method / Result
Extensive experiments demonstrate consistent state-of-the-art performance.
Limitations
The reliance on Gaussian Risk Admission may complicate reproducibility due to the need for precise modeling of multi-view representations.
Paper to code
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