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Prompt Engineering On Dtd

Metrics

Harmonic mean

Results

Performance results of various models on this benchmark

Model Name	Harmonic mean	Paper Title	Repository
MetaPrompt	68.35	Learning Domain Invariant Prompt for Vision-Language Models
DePT	71.09	DePT: Decoupled Prompt Tuning	-
CLIP	56.37	Learning Transferable Visual Models From Natural Language Supervision
CoCoOp	64.85	Conditional Prompt Learning for Vision-Language Models
CoPrompt	72.79	Consistency-guided Prompt Learning for Vision-Language Models
RPO	68.61	Read-only Prompt Optimization for Vision-Language Few-shot Learning
PromptKD	77.94	PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
MMRL	73.82	MMRL: Multi-Modal Representation Learning for Vision-Language Models
MaPLe	68.16	MaPLe: Multi-modal Prompt Learning
HPT	72.16	Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
HPT++	74.23	HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
ProMetaR	72.31	Prompt Learning via Meta-Regularization
PromptSRC	71.75	Self-regulating Prompts: Foundational Model Adaptation without Forgetting

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