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  4. Prompt Engineering On Imagenet A

Prompt Engineering On Imagenet A

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

模型名称
Top-1 accuracy %
Paper TitleRepository
HPT50.85Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
MaPLe50.90MaPLe: Multi-modal Prompt Learning
PromptSRC50.90Self-regulating Prompts: Foundational Model Adaptation without Forgetting
CoCoOp50.63Conditional Prompt Learning for Vision-Language Models
POMP51.6Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition
MMRL51.20MMRL: Multi-Modal Representation Learning for Vision-Language Models
CoPrompt50.50Consistency-guided Prompt Learning for Vision-Language Models
CLIP47.77Learning Transferable Visual Models From Natural Language Supervision
HPT++51.18HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
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