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Prompt Engineering On Oxford 102 Flower

Metrics

Harmonic mean

Results

Performance results of various models on this benchmark

		Paper Title	Repository
PromptKD	90.24	PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
HPT	87.16	Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
MMRL	86.78	MMRL: Multi-Modal Representation Learning for Vision-Language Models
ProMetaR	86.70	Prompt Learning via Meta-Regularization
DePT	86.46	DePT: Decoupled Prompt Tuning
PromptSRC	85.95	Self-regulating Prompts: Foundational Model Adaptation without Forgetting
HPT++	85.85	HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
CoPrompt	85.71	Consistency-guided Prompt Learning for Vision-Language Models
MetaPrompt	84.52	Learning Domain Invariant Prompt for Vision-Language Models
RPO	84.50	Read-only Prompt Optimization for Vision-Language Few-shot Learning
MaPLe	82.56	MaPLe: Multi-modal Prompt Learning
CoCoOp	81.71	Conditional Prompt Learning for Vision-Language Models
CLIP	74.83	Learning Transferable Visual Models From Natural Language Supervision

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Prompt Engineering On Oxford 102 Flower | SOTA | HyperAI