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Prompt Engineering On Imagenet R

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
MaPLe76.98MaPLe: Multi-modal Prompt Learning
POMP77.9Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition
HPT77.38Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
CoPrompt77.51Consistency-guided Prompt Learning for Vision-Language Models
PromptSRC77.80Self-regulating Prompts: Foundational Model Adaptation without Forgetting
CoCoOP76.18Conditional Prompt Learning for Vision-Language Models
CLIP73.96Learning Transferable Visual Models From Natural Language Supervision
HPT++77.52HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
MMRL77.53MMRL: Multi-Modal Representation Learning for Vision-Language Models
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