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Learning to Prompt for Vision-Language Models
Kaiyang Zhou
*
, Jingkang Yang
, Chen Change Loy
, Ziwei Liu
*
Corresponding author for this work
Department of Computer Science
Research output
:
Contribution to journal
›
Journal article
›
peer-review
3368
Citations (Scopus)
Overview
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Dive into the research topics of 'Learning to Prompt for Vision-Language Models'. Together they form a unique fingerprint.
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Keyphrases
Vision-language Models
100%
Image Recognition
66%
Downstream Task
66%
Prompt Engineering
66%
Recent Advances
33%
Natural Language Processing
33%
Recognition Task
33%
Generalization Performance
33%
Optimization Model
33%
Learning-based
33%
Learning Representations
33%
Representation Learning
33%
Domain Generalization
33%
Discretized
33%
Learning Research
33%
Impact Performance
33%
Class-specific
33%
Natural Language
33%
Classifier Weights
33%
Pre-Trained Parameters
33%
Over 45
33%
Domain Expertise
33%
Prompt Learning
33%
Context Word
33%
Zero-shot Models
33%
Common Feature Space
33%
Zero-shot Transfer
33%
Average Gain
33%
Vision-language Pre-training
33%
Computer Science
Language Modeling
100%
Representation Learning
66%
Prompt Engineering
66%
Zero-Shot Learning
66%
Natural Language Processing
33%
Generalization Performance
33%
Feature Space
33%
Domain Expertise
33%
Requires Optimization
33%
Prompt Learning
33%