Bi-Modality Individual-Aware Prompt Tuning for Visual-Language Model

Prompt tuning is a valuable technique for adapting visual language models (VLMs) to different downstream tasks, such as domain generalization and learning from a few examples. Previous methods have utilized Context Optimization approaches to deduce domain-shared or cross-modality prompt tokens, which enhance generalization and discriminative ability in textual or visual contexts. However, these prompt tokens, inferred from training data, cannot adapt perfectly to the distribution of the test dataset. This work introduces a novel approach called Bi-modality Individual-aware Prompt Tuning (BIP) by explicitly incorporating the individual's essential prior knowledge into the learnable prompt to enhance their discriminability and generalization. The critical insight of BIP involves applying the Textual Knowledge Embedding (TKE) and Visual Knowledge Embedding (VKE) models to project the class-aware textual essential knowledge and the instance-aware essential knowledge into the class-aware prompt and instance-aware prompt, referred to as Textual-level Class-aware Prompt tuning (TCP) and Visual-level Instance-aware Prompt tuning (VIP). On the one hand, TCP integrates the generated class-aware prompts into the Text Encoder to produce a dynamic class-aware classifier to improve generalization on unseen domains. On the other hand, VIP uses the instance-aware prompt to generate the dynamic visual embedding of each instance, thereby enhancing the discriminative capability of visual embedding. Comprehensive evaluations demonstrate that BIP can be used as a plug-and-play module easily integrated with existing methods and achieves superior performance on 15 benchmarks across four tasks.