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SOTA
Prompt Engineering
Prompt Engineering On Oxford 102 Flower
Prompt Engineering On Oxford 102 Flower
평가 지표
Harmonic mean
평가 결과
이 벤치마크에서 각 모델의 성능 결과
Columns
모델 이름
Harmonic mean
Paper Title
Repository
CLIP
74.83
Learning Transferable Visual Models From Natural Language Supervision
PromptSRC
85.95
Self-regulating Prompts: Foundational Model Adaptation without Forgetting
CoPrompt
85.71
Consistency-guided Prompt Learning for Vision-Language Models
HPT
87.16
Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
MetaPrompt
84.52
Learning Domain Invariant Prompt for Vision-Language Models
ProMetaR
86.70
Prompt Learning via Meta-Regularization
DePT
86.46
DePT: Decoupled Prompt Tuning
-
CoCoOp
81.71
Conditional Prompt Learning for Vision-Language Models
RPO
84.50
Read-only Prompt Optimization for Vision-Language Few-shot Learning
HPT++
85.85
HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
PromptKD
90.24
PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
MaPLe
82.56
MaPLe: Multi-modal Prompt Learning
MMRL
86.78
MMRL: Multi-Modal Representation Learning for Vision-Language Models
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