Prompt Engineering On Ucf101

Metrics

Harmonic mean

Results

Performance results of various models on this benchmark

		Paper Title
PromptKD	86.10	PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
MMRL	83.89	MMRL: Multi-Modal Representation Learning for Vision-Language Models
HPT++	83.81	HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
ProMetaR	83.25	Prompt Learning via Meta-Regularization
HPT	83.16	Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
CoPrompt	83.07	Consistency-guided Prompt Learning for Vision-Language Models
PromptSRC	82.74	Self-regulating Prompts: Foundational Model Adaptation without Forgetting
DePT	82.46	DePT: Decoupled Prompt Tuning
MetaPrompt	81.35	Learning Domain Invariant Prompt for Vision-Language Models
MaPLe	80.82	MaPLe: Multi-modal Prompt Learning
RPO	79.34	Read-only Prompt Optimization for Vision-Language Few-shot Learning
CoCoOp	77.64	Conditional Prompt Learning for Vision-Language Models
CLIP	73.85	Learning Transferable Visual Models From Natural Language Supervision

0 of 13 row(s) selected.

Prompt Engineering On Ucf101 | SOTA | HyperAI