Prompt Engineering On Imagenet R

Metrics

Top-1 accuracy %

Results

Performance results of various models on this benchmark

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

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Prompt Engineering On Imagenet R

Metrics

Top-1 accuracy %

Results

Performance results of various models on this benchmark

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

0 of 9 row(s) selected.