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홈뉴스연구 논문튜토리얼데이터셋백과사전SOTALLM 모델GPU 랭킹컨퍼런스
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  2. SOTA
  3. 프롬프트 엔지니어링
  4. Prompt Engineering On Imagenet

Prompt Engineering On Imagenet

평가 지표

Harmonic mean

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름
Harmonic mean
Paper TitleRepository
PromptKD77.62PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
ProMetaR74.09Prompt Learning via Meta-Regularization
MaPLe73.47MaPLe: Multi-modal Prompt Learning
PromptSRC74.01Self-regulating Prompts: Foundational Model Adaptation without Forgetting
RPO74.00Read-only Prompt Optimization for Vision-Language Few-shot Learning
Customized Ensemble75.49Beyond Sole Strength: Customized Ensembles for Generalized Vision-Language Models
CLIP70.22Learning Transferable Visual Models From Natural Language Supervision
MetaPrompt74.02Learning Domain Invariant Prompt for Vision-Language Models
MMRL74.45MMRL: Multi-Modal Representation Learning for Vision-Language Models
HPT74.17Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
DePT74.02DePT: Decoupled Prompt Tuning
HPT++74.24HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
CoCoOp73.10Conditional Prompt Learning for Vision-Language Models
CoPrompt74.33Consistency-guided Prompt Learning for Vision-Language Models
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한국어

소개

회사 소개데이터셋 도움말

제품

뉴스튜토리얼데이터셋백과사전

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