DeCoOp: Robust Prompt Tuning with Out-of-Distribution Detection
ICML, 2024

Vision-language models (VLMs), such as CLIP, have demonstrated impressive zero-shot capabilities for various downstream tasks. Their performance can be further enhanced through few-shot prompt tuning methods. However, current studies evaluate the performance of learned prompts separately on base and new classes. This evaluation lacks practicality for real-world applications since downstream tasks cannot determine whether the data belongs to base or new classes in advance. In this paper, we explore a problem setting called <b><em>O</em>*pen-world </b>P<b>rompt </b>T<b>uning<em> (OPT), which involves tuning prompts on base classes and evaluating on a combination of base and new classes. By introducing </em></b>De<b>composed </b>P<b>rompt </b>T<b>uning<em> framework (DePT), we theoretically demonstrate that OPT can be solved by incorporating out-of-distribution detection into prompt tuning, thereby enhancing the base-to-new discriminability. Based on DePT, we present a novel prompt tuning approach, namely, </em></b>De<b>composed </b>Co<b>ntext </b>Op*<em>timization</em> (DeCoOp), which introduces new-class detectors and sub-classifiers to further enhance the base-class and new-class discriminability. Experimental results on 11 benchmark datasets validate the effectiveness of DePT and demonstrate that DeCoOp outperforms current state-of-the-art methods, providing a significant 2% average accuracy improvement.

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