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Published on: December 6, 2024
CuTCP: Custom Text Generation-based Class-aware Prompt Tuning for visual-language models.
Min Huang1, Chen Yang2, Xiaoyan Yu1
1Zhengzhou University of Light Industry, Zhengzhou, 450001, China.
Custom Text Generation-based Class-aware Prompt Tuning (CuTCP) enhances visual-language models by generating specific prompts, improving fine-grained classification and generalization for new categories.
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Area of Science:
- Artificial Intelligence
- Computer Vision
- Natural Language Processing
Background:
- Visual-language models (VLMs) integrate visual and linguistic data for cross-modal reasoning.
- Prompt learning is a common technique for fine-tuning VLMs for downstream tasks.
- Existing class-aware prompt tuning methods may struggle with fine-grained category distinctions due to fixed text templates.
Purpose of the Study:
- To introduce Custom Text Generation-based Class-aware Prompt Tuning (CuTCP) for improved VLM generalization.
- To enhance the adaptability of VLMs to fine-grained classification tasks.
- To overcome the limitations of fixed prompt templates in prior methods.
Main Methods:
- CuTCP utilizes large language models to generate descriptive, category-specific prompts.
- These generated prompts embed richer semantic information compared to generic templates.
- The method was evaluated across 11 diverse image datasets.
Main Results:
- CuTCP demonstrated a 0.74% improvement on new classes and a 0.44% improvement in overall harmonic mean compared to TCP.
- The approach significantly enhances model adaptability and generalization capabilities.
- Strong performance was observed particularly in fine-grained classification tasks.
Conclusions:
- CuTCP effectively addresses the limitations of general prompt templates in VLMs.
- The proposed method improves the ability of VLMs to differentiate between known and unseen categories.
- CuTCP offers a more adaptable and generalizable solution for VLM fine-tuning, especially for complex classification scenarios.

