Vision language models improve by selectively adapting at test time
To Adapt or Not to Adapt? Selective Adaptation for Vision-Language Models
Computer Vision and Pattern RecognitionMachine Learning
Summary
When using vision-language models, adapting them during testing can sometimes help or harm predictions. The researchers found that adapting the model for every single test example isn’t always useful and can even change correct answers into wrong ones. They introduced a method called Cross-Augmentation Similarity (CAS) to decide when to adapt the model based on how consistent its predictions are with slightly altered versions of the same input. By skipping unnecessary changes, their approach often keeps or improves accuracy while reducing the amount of adaptation needed.
vision-language modelstest-time adaptationdistribution shiftsmodel predictionsaugmentationCross-Augmentation Similarityselective adaptationinferenceaccuracy
Authors
Siru Jiang, Yuwei Liang, Jian Liang, Ran He, Tieniu Tan
Abstract
Test-time adaptation (TTA) has emerged as a prominent strategy for adapting vision-language models to distribution shifts during inference. We conduct a per-sample analysis of model predictions before and after adaptation, and observe two failure modes in existing TTA methods that echo previous work. Adaptations are frequently negligible, yielding no change in the model's predictions, and more severely, they can be detrimental by flipping previously correct predictions to incorrect ones. This naturally raises a question: Can we identify and skip such negligible or harmful adaptations? In this work, we introduce a new problem of selective adaptation, which aims to determine whether a given test sample should undergo adaptation or be skipped. To this end, we propose Cross-Augmentation Similarity (CAS), a simple baseline that performs adaptation only when predictions across augmented views exhibit low similarity. Notably, CAS not only preserves but in some cases improves overall accuracy, even when skipping nearly 85% of the adaptation process. We hope other researchers will explore this new direction and surpass the performance of our baseline. Our code is available at https://github.com/sirujiang/selective-adaptation.