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

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HyperAI초신경

Prompt Engineering On Imagenet R

평가 지표

Top-1 accuracy %

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	Top-1 accuracy %	Paper Title	Repository
MaPLe	76.98	MaPLe: Multi-modal Prompt Learning	-
POMP	77.9	Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition	-
HPT	77.38	Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models	-
CoPrompt	77.51	Consistency-guided Prompt Learning for Vision-Language Models	-
PromptSRC	77.80	Self-regulating Prompts: Foundational Model Adaptation without Forgetting	-
CoCoOP	76.18	Conditional Prompt Learning for Vision-Language Models	-
CLIP	73.96	Learning Transferable Visual Models From Natural Language Supervision	-
HPT++	77.52	HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling	-
MMRL	77.53	MMRL: Multi-Modal Representation Learning for Vision-Language Models	-

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Prompt Engineering On Imagenet R | SOTA | HyperAI초신경