Prompt Engineering On Stanford Cars 1

Metrics

Harmonic mean

Results

Performance results of various models on this benchmark

		Paper Title
PromptKD	83.13	PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
MMRL	78.06	MMRL: Multi-Modal Representation Learning for Vision-Language Models
DePT	77.79	DePT: Decoupled Prompt Tuning
ProMetaR	76.72	Prompt Learning via Meta-Regularization
PromptSRC	76.58	Self-regulating Prompts: Foundational Model Adaptation without Forgetting
CoPrompt	75.66	Consistency-guided Prompt Learning for Vision-Language Models
HPT++	75.59	HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
HPT	75.57	Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
MetaPrompt	75.48	Learning Domain Invariant Prompt for Vision-Language Models
RPO	74.69	Read-only Prompt Optimization for Vision-Language Few-shot Learning
MaPLe	73.47	MaPLe: Multi-modal Prompt Learning
CoCoOp	72.01	Conditional Prompt Learning for Vision-Language Models
CLIP	68.65	Learning Transferable Visual Models From Natural Language Supervision

0 of 13 row(s) selected.

Prompt Engineering On Stanford Cars 1 | SOTA | HyperAI