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SOTA
프롬프트 엔지니어링
Prompt Engineering On Ucf101
Prompt Engineering On Ucf101
평가 지표
Harmonic mean
평가 결과
이 벤치마크에서 각 모델의 성능 결과
Columns
모델 이름
Harmonic mean
Paper Title
Repository
PromptKD
86.10
PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
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MetaPrompt
81.35
Learning Domain Invariant Prompt for Vision-Language Models
-
MaPLe
80.82
MaPLe: Multi-modal Prompt Learning
-
PromptSRC
82.74
Self-regulating Prompts: Foundational Model Adaptation without Forgetting
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HPT
83.16
Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
-
MMRL
83.89
MMRL: Multi-Modal Representation Learning for Vision-Language Models
-
CoPrompt
83.07
Consistency-guided Prompt Learning for Vision-Language Models
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ProMetaR
83.25
Prompt Learning via Meta-Regularization
-
CLIP
73.85
Learning Transferable Visual Models From Natural Language Supervision
-
HPT++
83.81
HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
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CoCoOp
77.64
Conditional Prompt Learning for Vision-Language Models
-
RPO
79.34
Read-only Prompt Optimization for Vision-Language Few-shot Learning
-
DePT
82.46
DePT: Decoupled Prompt Tuning
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