Prompt Engineering On Imagenet V2
평가 지표
Top-1 accuracy %
평가 결과
이 벤치마크에서 각 모델의 성능 결과
모델 이름 | Top-1 accuracy % | Paper Title | Repository |
|---|---|---|---|
| CoCoOp | 64.07 | Conditional Prompt Learning for Vision-Language Models | - |
| MaPLe | 64.07 | MaPLe: Multi-modal Prompt Learning | - |
| POMP | 63.8 | - | - |
| CLIP | 60.83 | Learning Transferable Visual Models From Natural Language Supervision | - |
| HPT | 65.25 | Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models | - |
| MMRL | 64.47 | MMRL: Multi-Modal Representation Learning for Vision-Language Models | - |
| HPT++ | 65.31 | HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling | - |
| PromptSRC | 64.35 | Self-regulating Prompts: Foundational Model Adaptation without Forgetting | - |
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