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SOTA
Prompt Engineering
Prompt Engineering On Imagenet A
Prompt Engineering On Imagenet A
평가 지표
Top-1 accuracy %
평가 결과
이 벤치마크에서 각 모델의 성능 결과
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모델 이름
Top-1 accuracy %
Paper Title
Repository
HPT
50.85
Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
MaPLe
50.90
MaPLe: Multi-modal Prompt Learning
PromptSRC
50.90
Self-regulating Prompts: Foundational Model Adaptation without Forgetting
CoCoOp
50.63
Conditional Prompt Learning for Vision-Language Models
POMP
51.6
Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition
MMRL
51.20
MMRL: Multi-Modal Representation Learning for Vision-Language Models
CoPrompt
50.50
Consistency-guided Prompt Learning for Vision-Language Models
CLIP
47.77
Learning Transferable Visual Models From Natural Language Supervision
HPT++
51.18
HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
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