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홈 뉴스 최신 연구 논문 튜토리얼 데이터셋 백과사전 SOTA LLM 모델 GPU 랭킹 컨퍼런스

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Prompt Engineering On Caltech 101

평가 지표

Harmonic mean

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	Harmonic mean	Paper Title	Repository
ProMetaR	96.16	Prompt Learning via Meta-Regularization	-
HPT++	96.96	HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling	-
MaPLe	96.02	MaPLe: Multi-modal Prompt Learning	-
CoCoOp	95.84	Conditional Prompt Learning for Vision-Language Models	-
PromptSRC	96.02	Self-regulating Prompts: Foundational Model Adaptation without Forgetting	-
HPT	96.65	Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models	-
CLIP	95.40	Learning Transferable Visual Models From Natural Language Supervision	-
CoPrompt	96.55	Consistency-guided Prompt Learning for Vision-Language Models	-
MetaPrompt	96.32	Learning Domain Invariant Prompt for Vision-Language Models	-
RPO	96.03	Read-only Prompt Optimization for Vision-Language Few-shot Learning	-
PromptKD	97.77	PromptKD: Unsupervised Prompt Distillation for Vision-Language Models	-
MMRL	96.68	MMRL: Multi-Modal Representation Learning for Vision-Language Models	-
DePT	96.28	DePT: Decoupled Prompt Tuning	-

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Prompt Engineering On Caltech 101 | SOTA | HyperAI초신경