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홈뉴스연구 논문튜토리얼데이터셋백과사전SOTALLM 모델GPU 랭킹컨퍼런스
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  3. 프롬프트 엔지니어링
  4. Prompt Engineering On Ucf101

Prompt Engineering On Ucf101

평가 지표

Harmonic mean

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름
Harmonic mean
Paper TitleRepository
PromptKD86.10PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
MetaPrompt81.35Learning Domain Invariant Prompt for Vision-Language Models
MaPLe80.82MaPLe: Multi-modal Prompt Learning
PromptSRC82.74Self-regulating Prompts: Foundational Model Adaptation without Forgetting
HPT83.16Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
MMRL83.89MMRL: Multi-Modal Representation Learning for Vision-Language Models
CoPrompt83.07Consistency-guided Prompt Learning for Vision-Language Models
ProMetaR83.25Prompt Learning via Meta-Regularization
CLIP73.85Learning Transferable Visual Models From Natural Language Supervision
HPT++83.81HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
CoCoOp77.64Conditional Prompt Learning for Vision-Language Models
RPO79.34Read-only Prompt Optimization for Vision-Language Few-shot Learning
DePT82.46DePT: Decoupled Prompt Tuning
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소개

회사 소개데이터셋 도움말

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뉴스튜토리얼데이터셋백과사전

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