TCP:Textual-based Class-aware Prompt tuning for Visual-Language Model
- URL: http://arxiv.org/abs/2311.18231v2
- Date: Wed, 13 Mar 2024 01:42:51 GMT
- Title: TCP:Textual-based Class-aware Prompt tuning for Visual-Language Model
- Authors: Hantao Yao, Rui Zhang, Changsheng Xu
- Abstract summary: We present a Textual-based Class-aware Prompt tuning( TCP) that explicitly incorporates prior knowledge about classes to enhance their discriminability.
TCP consistently achieves superior performance while demanding less training time.
- Score: 78.77544632773404
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Prompt tuning represents a valuable technique for adapting pre-trained
visual-language models (VLM) to various downstream tasks. Recent advancements
in CoOp-based methods propose a set of learnable domain-shared or
image-conditional textual tokens to facilitate the generation of task-specific
textual classifiers. However, those textual tokens have a limited
generalization ability regarding unseen domains, as they cannot dynamically
adjust to the distribution of testing classes. To tackle this issue, we present
a novel Textual-based Class-aware Prompt tuning(TCP) that explicitly
incorporates prior knowledge about classes to enhance their discriminability.
The critical concept of TCP involves leveraging Textual Knowledge Embedding
(TKE) to map the high generalizability of class-level textual knowledge into
class-aware textual tokens. By seamlessly integrating these class-aware prompts
into the Text Encoder, a dynamic class-aware classifier is generated to enhance
discriminability for unseen domains. During inference, TKE dynamically
generates class-aware prompts related to the unseen classes. Comprehensive
evaluations demonstrate that TKE serves as a plug-and-play module effortlessly
combinable with existing methods. Furthermore, TCP consistently achieves
superior performance while demanding less training time.
Code:https://github.com/htyao89/Textual-based_Class-aware_prompt_tuning/
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