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Prompt Engineering On Ucf101

Metrics

Harmonic mean

Results

Performance results of various models on this benchmark

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

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Prompt Engineering On Ucf101 | SOTA | HyperAI