Prompt Engineering On Imagenet S

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Top-1 accuracy %

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

		Paper Title
POMP	49.8	Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition
PromptSRC	49.55	Self-regulating Prompts: Foundational Model Adaptation without Forgetting
CoPrompt	49.43	Consistency-guided Prompt Learning for Vision-Language Models
HPT	49.36	Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
HPT++	49.28	HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
MMRL	49.17	MMRL: Multi-Modal Representation Learning for Vision-Language Models
MaPLe	49.15	MaPLe: Multi-modal Prompt Learning
CoCoOp	48.75	Conditional Prompt Learning for Vision-Language Models
CLIP	46.15	Learning Transferable Visual Models From Natural Language Supervision

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Prompt Engineering On Imagenet S | SOTA | HyperAI