Prompt Engineering On Caltech 101

Metriken

Harmonic mean

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

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

0 of 13 row(s) selected.

Prompt Engineering On Caltech 101 | SOTA | HyperAI