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Prompt Engineering
Prompt Engineering On Eurosat
Prompt Engineering On Eurosat
Metrics
Harmonic mean
Results
Performance results of various models on this benchmark
Columns
Model Name
Harmonic mean
Paper Title
Repository
PromptSRC
82.32
Self-regulating Prompts: Foundational Model Adaptation without Forgetting
CoPrompt
85.84
Consistency-guided Prompt Learning for Vision-Language Models
MMRL
87.21
MMRL: Multi-Modal Representation Learning for Vision-Language Models
HPT++
87.36
HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
MaPLe
82.35
MaPLe: Multi-modal Prompt Learning
DePT
84.88
DePT: Decoupled Prompt Tuning
-
PromptKD
89.14
PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
MetaPrompt
83.38
Learning Domain Invariant Prompt for Vision-Language Models
CLIP
60.03
Learning Transferable Visual Models From Natural Language Supervision
RPO
76.79
Read-only Prompt Optimization for Vision-Language Few-shot Learning
CoCoOp
71.21
Conditional Prompt Learning for Vision-Language Models
HPT
84.82
Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
ProMetaR
85.30
Prompt Learning via Meta-Regularization
0 of 13 row(s) selected.
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