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SOTA
Prompt-Engineering
Prompt Engineering On Fgvc Aircraft
Prompt Engineering On Fgvc Aircraft
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Harmonic mean
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
Harmonic mean
Paper Title
PromptKD
45.17
PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
HPT++
41.33
HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
MMRL
41.15
MMRL: Multi-Modal Representation Learning for Vision-Language Models
DePT
40.73
DePT: Decoupled Prompt Tuning
HPT
40.28
Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
ProMetaR
40.25
Prompt Learning via Meta-Regularization
PromptSRC
40.15
Self-regulating Prompts: Foundational Model Adaptation without Forgetting
CoPrompt
39.76
Consistency-guided Prompt Learning for Vision-Language Models
MetaPrompt
38.24
Learning Domain Invariant Prompt for Vision-Language Models
MaPLe
36.50
MaPLe: Multi-modal Prompt Learning
RPO
35.70
Read-only Prompt Optimization for Vision-Language Few-shot Learning
CLIP
31.09
Learning Transferable Visual Models From Natural Language Supervision
CoCoOp
27.74
Conditional Prompt Learning for Vision-Language Models
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Prompt Engineering On Fgvc Aircraft | SOTA | HyperAI