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SotA/Natural Language Processing/Prompt Engineering/ImageNet-S

Prompt Engineering on ImageNet-S

Metric: Top-1 accuracy % (higher is better)

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#Model↕Top-1 accuracy %▼Extra DataPaperDate↕Code
1POMP49.8NoPrompt Pre-Training with Twenty-Thousand Classes...2023-04-10Code
2PromptSRC49.55NoSelf-regulating Prompts: Foundational Model Adap...2023-07-13Code
3CoPrompt49.43NoConsistency-guided Prompt Learning for Vision-La...2023-06-01Code
4HPT49.36NoLearning Hierarchical Prompt with Structured Lin...2023-12-11Code
5HPT++49.28NoHPT++: Hierarchically Prompting Vision-Language ...2024-08-27Code
6MMRL49.17NoMMRL: Multi-Modal Representation Learning for Vi...2025-03-11Code
7MaPLe49.15NoMaPLe: Multi-modal Prompt Learning2022-10-06Code
8CoCoOp48.75NoConditional Prompt Learning for Vision-Language ...2022-03-10Code
9CLIP46.15NoLearning Transferable Visual Models From Natural...2021-02-26Code