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홈뉴스연구 논문튜토리얼데이터셋백과사전SOTALLM 모델GPU 랭킹컨퍼런스
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  3. 프롬프트 엔지니어링
  4. Prompt Engineering On Eurosat

Prompt Engineering On Eurosat

평가 지표

Harmonic mean

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름
Harmonic mean
Paper TitleRepository
PromptSRC82.32Self-regulating Prompts: Foundational Model Adaptation without Forgetting
CoPrompt85.84Consistency-guided Prompt Learning for Vision-Language Models
MMRL87.21MMRL: Multi-Modal Representation Learning for Vision-Language Models
HPT++87.36HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
MaPLe82.35MaPLe: Multi-modal Prompt Learning
DePT84.88DePT: Decoupled Prompt Tuning
PromptKD89.14PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
MetaPrompt83.38Learning Domain Invariant Prompt for Vision-Language Models
CLIP60.03Learning Transferable Visual Models From Natural Language Supervision
RPO76.79Read-only Prompt Optimization for Vision-Language Few-shot Learning
CoCoOp71.21Conditional Prompt Learning for Vision-Language Models
HPT84.82Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
ProMetaR85.30Prompt Learning via Meta-Regularization
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한국어

소개

회사 소개데이터셋 도움말

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뉴스튜토리얼데이터셋백과사전

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