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홈뉴스최신 연구 논문튜토리얼데이터셋백과사전SOTALLM 모델GPU 랭킹컨퍼런스
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소개
한국어
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  1. 홈
  2. SOTA
  3. 프롬프트 엔지니어링
  4. Prompt Engineering On Sun397

Prompt Engineering On Sun397

평가 지표

Harmonic mean

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름
Harmonic mean
Paper TitleRepository
ProMetaR80.82Prompt Learning via Meta-Regularization-
DePT81.06DePT: Decoupled Prompt Tuning-
PromptKD82.60PromptKD: Unsupervised Prompt Distillation for Vision-Language Models-
CoCoOp78.27Conditional Prompt Learning for Vision-Language Models-
CoPrompt81.31Consistency-guided Prompt Learning for Vision-Language Models-
HPT80.88Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models-
HPT++81.11HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling-
PromptSRC80.52Self-regulating Prompts: Foundational Model Adaptation without Forgetting-
MMRL81.20MMRL: Multi-Modal Representation Learning for Vision-Language Models-
MaPLe79.75MaPLe: Multi-modal Prompt Learning-
RPO79.18Read-only Prompt Optimization for Vision-Language Few-shot Learning-
CLIP72.23Learning Transferable Visual Models From Natural Language Supervision-
MetaPrompt80.62Learning Domain Invariant Prompt for Vision-Language Models-
0 of 13 row(s) selected.
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한국어

소개

회사 소개데이터셋 도움말

제품

뉴스튜토리얼데이터셋백과사전

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