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Prompt Engineering On Caltech 101

Métriques

Harmonic mean

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Harmonic mean	Paper Title	Repository
ProMetaR	96.16	Prompt Learning via Meta-Regularization
HPT++	96.96	HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
MaPLe	96.02	MaPLe: Multi-modal Prompt Learning
CoCoOp	95.84	Conditional Prompt Learning for Vision-Language Models
PromptSRC	96.02	Self-regulating Prompts: Foundational Model Adaptation without Forgetting
HPT	96.65	Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
CLIP	95.40	Learning Transferable Visual Models From Natural Language Supervision
CoPrompt	96.55	Consistency-guided Prompt Learning for Vision-Language Models
MetaPrompt	96.32	Learning Domain Invariant Prompt for Vision-Language Models
RPO	96.03	Read-only Prompt Optimization for Vision-Language Few-shot Learning
PromptKD	97.77	PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
MMRL	96.68	MMRL: Multi-Modal Representation Learning for Vision-Language Models
DePT	96.28	DePT: Decoupled Prompt Tuning	-

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