Efficient Test-Time Prompt Tuning for Vision-Language Models

Understand

Vision-language models have showcased impressive zero-shot classification capabilities when equipped with suitable text prompts.

• Previous studies have shown the effectiveness of test-time prompt tuning; however, these methods typically require per-image prompt adaptation during inference, which incurs high computational budgets and limits scalability and practical deployment.
• To overcome this issue, we introduce Self-TPT, a novel framework leveraging Self-supervised learning for efficient Test-time Prompt Tuning.
• The key aspect of Self-TPT is that it turns to efficient predefined class adaptation via self-supervised learning, thus avoiding computation-heavy per-image adaptation at inference.