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홈
SOTA
Prompt Engineering
Prompt Engineering On Eurosat
Prompt Engineering On Eurosat
평가 지표
Harmonic mean
평가 결과
이 벤치마크에서 각 모델의 성능 결과
Columns
모델 이름
Harmonic mean
Paper Title
Repository
PromptSRC
82.32
Self-regulating Prompts: Foundational Model Adaptation without Forgetting
CoPrompt
85.84
Consistency-guided Prompt Learning for Vision-Language Models
MMRL
87.21
MMRL: Multi-Modal Representation Learning for Vision-Language Models
HPT++
87.36
HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
MaPLe
82.35
MaPLe: Multi-modal Prompt Learning
DePT
84.88
DePT: Decoupled Prompt Tuning
-
PromptKD
89.14
PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
MetaPrompt
83.38
Learning Domain Invariant Prompt for Vision-Language Models
CLIP
60.03
Learning Transferable Visual Models From Natural Language Supervision
RPO
76.79
Read-only Prompt Optimization for Vision-Language Few-shot Learning
CoCoOp
71.21
Conditional Prompt Learning for Vision-Language Models
HPT
84.82
Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
ProMetaR
85.30
Prompt Learning via Meta-Regularization
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