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홈뉴스연구 논문튜토리얼데이터셋백과사전SOTALLM 모델GPU 랭킹컨퍼런스
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한국어
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  2. SOTA
  3. 프롬프트 엔지니어링
  4. Prompt Engineering On Oxford 102 Flower

Prompt Engineering On Oxford 102 Flower

평가 지표

Harmonic mean

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름
Harmonic mean
Paper TitleRepository
CLIP74.83Learning Transferable Visual Models From Natural Language Supervision
PromptSRC85.95Self-regulating Prompts: Foundational Model Adaptation without Forgetting
CoPrompt85.71Consistency-guided Prompt Learning for Vision-Language Models
HPT87.16Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
MetaPrompt84.52Learning Domain Invariant Prompt for Vision-Language Models
ProMetaR86.70Prompt Learning via Meta-Regularization
DePT86.46DePT: Decoupled Prompt Tuning
CoCoOp81.71Conditional Prompt Learning for Vision-Language Models
RPO84.50Read-only Prompt Optimization for Vision-Language Few-shot Learning
HPT++85.85HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
PromptKD90.24PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
MaPLe82.56MaPLe: Multi-modal Prompt Learning
MMRL86.78MMRL: Multi-Modal Representation Learning for Vision-Language Models
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한국어

소개

회사 소개데이터셋 도움말

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뉴스튜토리얼데이터셋백과사전

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