Prompt Engineering On Eurosat

Metrics

Harmonic mean

Results

Performance results of various models on this benchmark

		Paper Title
PromptKD	89.14	PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
HPT++	87.36	HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
MMRL	87.21	MMRL: Multi-Modal Representation Learning for Vision-Language Models
CoPrompt	85.84	Consistency-guided Prompt Learning for Vision-Language Models
ProMetaR	85.30	Prompt Learning via Meta-Regularization
DePT	84.88	DePT: Decoupled Prompt Tuning
HPT	84.82	Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
MetaPrompt	83.38	Learning Domain Invariant Prompt for Vision-Language Models
MaPLe	82.35	MaPLe: Multi-modal Prompt Learning
PromptSRC	82.32	Self-regulating Prompts: Foundational Model Adaptation without Forgetting
RPO	76.79	Read-only Prompt Optimization for Vision-Language Few-shot Learning
CoCoOp	71.21	Conditional Prompt Learning for Vision-Language Models
CLIP	60.03	Learning Transferable Visual Models From Natural Language Supervision

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Prompt Engineering On Eurosat | SOTA | HyperAI