Prompt Engineering On Sun397

Metrics

Harmonic mean

Results

Performance results of various models on this benchmark

		Paper Title
PromptKD	82.60	PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
CoPrompt	81.31	Consistency-guided Prompt Learning for Vision-Language Models
MMRL	81.20	MMRL: Multi-Modal Representation Learning for Vision-Language Models
HPT++	81.11	HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
DePT	81.06	DePT: Decoupled Prompt Tuning
HPT	80.88	Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
ProMetaR	80.82	Prompt Learning via Meta-Regularization
MetaPrompt	80.62	Learning Domain Invariant Prompt for Vision-Language Models
PromptSRC	80.52	Self-regulating Prompts: Foundational Model Adaptation without Forgetting
MaPLe	79.75	MaPLe: Multi-modal Prompt Learning
RPO	79.18	Read-only Prompt Optimization for Vision-Language Few-shot Learning
CoCoOp	78.27	Conditional Prompt Learning for Vision-Language Models
CLIP	72.23	Learning Transferable Visual Models From Natural Language Supervision

0 of 13 row(s) selected.

Prompt Engineering On Sun397 | SOTA | HyperAI