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홈뉴스연구 논문튜토리얼데이터셋백과사전SOTALLM 모델GPU 랭킹컨퍼런스
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  4. Prompt Engineering On Food 101

Prompt Engineering On Food 101

평가 지표

Harmonic mean

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름
Harmonic mean
Paper TitleRepository
ProMetaR91.34Prompt Learning via Meta-Regularization
HPT++91.09HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
PromptSRC91.10Self-regulating Prompts: Foundational Model Adaptation without Forgetting
HPT91.01Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
MaPLe91.38MaPLe: Multi-modal Prompt Learning
MMRL91.03MMRL: Multi-Modal Representation Learning for Vision-Language Models
CoCoOp90.99Conditional Prompt Learning for Vision-Language Models
MetaPrompt91.29Learning Domain Invariant Prompt for Vision-Language Models
DePT91.22DePT: Decoupled Prompt Tuning
CoPrompt91.40Consistency-guided Prompt Learning for Vision-Language Models
PromptKD93.05PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
RPO90.58Read-only Prompt Optimization for Vision-Language Few-shot Learning
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한국어

소개

회사 소개데이터셋 도움말

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뉴스튜토리얼데이터셋백과사전

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