Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models
This addresses the challenge of maintaining generalization to unseen domains for vision-language models, offering a practical solution for zero-shot tasks without requiring additional training data.
The paper tackles the problem of zero-shot generalization in vision-language models by proposing test-time prompt tuning (TPT), which learns adaptive prompts from a single test sample without domain-specific training data, improving CLIP's zero-shot accuracy by 3.6% on average on natural distribution shifts and matching state-of-the-art methods on cross-dataset generalization.
Pre-trained vision-language models (e.g., CLIP) have shown promising zero-shot generalization in many downstream tasks with properly designed text prompts. Instead of relying on hand-engineered prompts, recent works learn prompts using the training data from downstream tasks. While effective, training on domain-specific data reduces a model's generalization capability to unseen new domains. In this work, we propose test-time prompt tuning (TPT), a method that can learn adaptive prompts on the fly with a single test sample. For image classification, TPT optimizes the prompt by minimizing the entropy with confidence selection so that the model has consistent predictions across different augmented views of each test sample. In evaluating generalization to natural distribution shifts, TPT improves the zero-shot top-1 accuracy of CLIP by 3.6% on average, surpassing previous prompt tuning approaches that require additional task-specific training data. In evaluating cross-dataset generalization with unseen categories, TPT performs on par with the state-of-the-art approaches that use additional training data. Project page: https://azshue.github.io/TPT.