Prototype-Based Test-Time Adaptation of Vision-Language Models
Test-time adaptation (TTA) has emerged as a promising paradigm for vision-language models (VLMs) to bridge the distribution gap between pre-training and test data. Recent works have focused on backpropagation-free TTA methods that rely on cache-based designs, but these introduce two key limitations. First, inference latency increases as the cache grows with the number of classes, leading to inefficiencies in large-scale settings. Second, suboptimal performance occurs when the cache contains insufficient or incorrect samples. In this paper, we present Prototype-Based Test-Time Adaptation (PTA), an efficient and effective TTA paradigm that uses a set of class-specific knowledge prototypes t...
| Authors | Zhaohong Huang, Yuxin Zhang, Wenjing Liu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 23 April 2026 |
| Last updated at source | 13 May 2026 |
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