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Prompt Engineering
Prompt Engineering On Caltech 101
Prompt Engineering On Caltech 101
Metrics
Harmonic mean
Results
Performance results of various models on this benchmark
Columns
Model Name
Harmonic mean
Paper Title
Repository
ProMetaR
96.16
Prompt Learning via Meta-Regularization
-
HPT++
96.96
HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
-
MaPLe
96.02
MaPLe: Multi-modal Prompt Learning
-
CoCoOp
95.84
Conditional Prompt Learning for Vision-Language Models
-
PromptSRC
96.02
Self-regulating Prompts: Foundational Model Adaptation without Forgetting
-
HPT
96.65
Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
-
CLIP
95.40
Learning Transferable Visual Models From Natural Language Supervision
-
CoPrompt
96.55
Consistency-guided Prompt Learning for Vision-Language Models
-
MetaPrompt
96.32
Learning Domain Invariant Prompt for Vision-Language Models
-
RPO
96.03
Read-only Prompt Optimization for Vision-Language Few-shot Learning
-
PromptKD
97.77
PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
-
MMRL
96.68
MMRL: Multi-Modal Representation Learning for Vision-Language Models
-
DePT
96.28
DePT: Decoupled Prompt Tuning
-
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