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SOTA
Prompt Engineering
Prompt Engineering On Imagenet S
Prompt Engineering On Imagenet S
评估指标
Top-1 accuracy %
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
Top-1 accuracy %
Paper Title
Repository
HPT++
49.28
HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
CoCoOp
48.75
Conditional Prompt Learning for Vision-Language Models
MMRL
49.17
MMRL: Multi-Modal Representation Learning for Vision-Language Models
CoPrompt
49.43
Consistency-guided Prompt Learning for Vision-Language Models
POMP
49.8
Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition
MaPLe
49.15
MaPLe: Multi-modal Prompt Learning
CLIP
46.15
Learning Transferable Visual Models From Natural Language Supervision
PromptSRC
49.55
Self-regulating Prompts: Foundational Model Adaptation without Forgetting
HPT
49.36
Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
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