Long-tailed Test-Time Adaptation for Vision-Language Models

Test-Time Adaptation (TTA) aims to further adapt models to unlabeled test sets arriving in a sequential datastream, thereby progressively strengthening the model's generalization ability. While existing TTA methods for Vision-Language Models (VLMs) are primarily designed and evaluated on (nearly) balanced dataset configurations, real-world test sets may exhibit a long-tailed distribution where major classes dominate the decision boundaries of minor classes, presenting unique challenges. As the first attempt to solve this problem, this paper proposes Long-tailed Test-Time Adaptation (dubbed as L-TTA), which consists of three co-designed mechanisms: Synergistic Prototypes (SyPs), Rebalancing Shortcuts (RSs), and Balanced Entropy Minimization (BEM). SyPs introduce two fine-grained prototypes to enrich tail classes with extra inter-class knowledge; RSs employ learnable shortcuts to achieve learnable adaptation, regularized by class re-allocation loss to enforce distinct feature clustering; BEM restrains excessive entropy minimization of confident classes with extra penalty term, with theoretical propositions to justify its rebalancing capabilities. Extensive experiments over 15 datasets under various long-tailed settings highlight the superior performance of L-TTA in both accuracy and class balancing.