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Prompt-Engineering
Prompt Engineering On Sun397
Prompt Engineering On Sun397
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Harmonic mean
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
Harmonic mean
Paper Title
PromptKD
82.60
PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
CoPrompt
81.31
Consistency-guided Prompt Learning for Vision-Language Models
MMRL
81.20
MMRL: Multi-Modal Representation Learning for Vision-Language Models
HPT++
81.11
HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
DePT
81.06
DePT: Decoupled Prompt Tuning
HPT
80.88
Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
ProMetaR
80.82
Prompt Learning via Meta-Regularization
MetaPrompt
80.62
Learning Domain Invariant Prompt for Vision-Language Models
PromptSRC
80.52
Self-regulating Prompts: Foundational Model Adaptation without Forgetting
MaPLe
79.75
MaPLe: Multi-modal Prompt Learning
RPO
79.18
Read-only Prompt Optimization for Vision-Language Few-shot Learning
CoCoOp
78.27
Conditional Prompt Learning for Vision-Language Models
CLIP
72.23
Learning Transferable Visual Models From Natural Language Supervision
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Prompt Engineering On Sun397 | SOTA | HyperAI