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Updated: Jul 9, 2026
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Published on: November 30, 2018
Enhancing Visual-Language Prompt Tuning Through Sparse Knowledge-Guided Context Optimization
1School of Information and Electrical Engineering, Hangzhou City University, Hangzhou 310015, China.
Entropy (Basel, Switzerland)
|March 28, 2025
Summary
Sparse Knowledge-guided Context Optimization (Sparse-KgCoOp) improves prompt tuning for visual-language models (VLMs). This method enhances generalization to new categories by reducing differences between adaptive and hand-crafted prompts, preserving core knowledge.
Area of Science:
- Computer Vision
- Natural Language Processing
- Artificial Intelligence
Background:
- Prompt tuning tailors visual-language models (VLMs) for specific tasks using task-specific tokens.
- Existing methods like CoOp can lead to poor generalization to unseen categories, overshadowing general knowledge.
Purpose of the Study:
- To address the base-novel dilemma in VLM prompt tuning.
- To enhance the generalization capability of adaptable prompts to novel categories.
Main Methods:
- Propose Sparse Knowledge-guided Context Optimization (Sparse-KgCoOp).
- Utilize sparsification operations to reduce differences between adaptive and hand-crafted prompts.
- Narrow the gap between dynamic and manually devised textual embeddings.
Main Results:
- Sparse-KgCoOp demonstrates efficient prompt tuning.
- The method effectively mitigates the erosion of fundamental knowledge during specialization.
- Experiments show improved generalization to unfamiliar categories.
Conclusions:
- Sparse-KgCoOp offers an effective solution for improving VLM prompt tuning generalization.
- The technique preserves foundational knowledge while enabling adaptability to new tasks and categories.
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