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Prompt Engineering
Prompt Engineering On Food 101
Prompt Engineering On Food 101
Metrics
Harmonic mean
Results
Performance results of various models on this benchmark
Columns
Model Name
Harmonic mean
Paper Title
Repository
ProMetaR
91.34
Prompt Learning via Meta-Regularization
HPT++
91.09
HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
PromptSRC
91.10
Self-regulating Prompts: Foundational Model Adaptation without Forgetting
HPT
91.01
Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
MaPLe
91.38
MaPLe: Multi-modal Prompt Learning
MMRL
91.03
MMRL: Multi-Modal Representation Learning for Vision-Language Models
CoCoOp
90.99
Conditional Prompt Learning for Vision-Language Models
MetaPrompt
91.29
Learning Domain Invariant Prompt for Vision-Language Models
DePT
91.22
DePT: Decoupled Prompt Tuning
-
CoPrompt
91.40
Consistency-guided Prompt Learning for Vision-Language Models
PromptKD
93.05
PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
RPO
90.58
Read-only Prompt Optimization for Vision-Language Few-shot Learning
0 of 12 row(s) selected.
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