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Prompt Engineering
Prompt Engineering On Sun397
Prompt Engineering On Sun397
Metrics
Harmonic mean
Results
Performance results of various models on this benchmark
Columns
Model Name
Harmonic mean
Paper Title
Repository
ProMetaR
80.82
Prompt Learning via Meta-Regularization
DePT
81.06
DePT: Decoupled Prompt Tuning
-
PromptKD
82.60
PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
CoCoOp
78.27
Conditional Prompt Learning for Vision-Language Models
CoPrompt
81.31
Consistency-guided Prompt Learning for Vision-Language Models
HPT
80.88
Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
HPT++
81.11
HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
PromptSRC
80.52
Self-regulating Prompts: Foundational Model Adaptation without Forgetting
MMRL
81.20
MMRL: Multi-Modal Representation Learning for Vision-Language Models
MaPLe
79.75
MaPLe: Multi-modal Prompt Learning
RPO
79.18
Read-only Prompt Optimization for Vision-Language Few-shot Learning
CLIP
72.23
Learning Transferable Visual Models From Natural Language Supervision
MetaPrompt
80.62
Learning Domain Invariant Prompt for Vision-Language Models
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