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SOTA
Prompt-Engineering
Prompt Engineering On Food 101
Prompt Engineering On Food 101
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Harmonic mean
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
Harmonic mean
Paper Title
PromptKD
93.05
PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
CoPrompt
91.40
Consistency-guided Prompt Learning for Vision-Language Models
MaPLe
91.38
MaPLe: Multi-modal Prompt Learning
ProMetaR
91.34
Prompt Learning via Meta-Regularization
MetaPrompt
91.29
Learning Domain Invariant Prompt for Vision-Language Models
DePT
91.22
DePT: Decoupled Prompt Tuning
PromptSRC
91.10
Self-regulating Prompts: Foundational Model Adaptation without Forgetting
HPT++
91.09
HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
MMRL
91.03
MMRL: Multi-Modal Representation Learning for Vision-Language Models
HPT
91.01
Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
CoCoOp
90.99
Conditional Prompt Learning for Vision-Language Models
RPO
90.58
Read-only Prompt Optimization for Vision-Language Few-shot Learning
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