Image Classification on ImageNet and 10 Target Datasets (test)
73.7ImageNet AccuracyMMRL
Evaluation results
| Method | Links | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MMRL2025.12 | 73.7 | 66.13 | 94.43 | 91.3 | 64.67 | 71.87 | 85.1 | 25.37 | 67 | 44.03 | 48.6 | 68.93 | |
| FARL2025.12 | 73.43 | 66.84 | 94.53 | 91.7 | 66.07 | 72.47 | 86.07 | 25.97 | 67.53 | 44.6 | 50.67 | 68.83 | |
| TCP2025.12 | 71.4 | 66.29 | 93.97 | 91.25 | 64.69 | 71.21 | 86.69 | 23.45 | 67.15 | 44.35 | 51.45 | 68.73 | |
| CoOp2025.12 | 71.02 | 65.74 | 94.43 | 90.14 | 65.32 | 71.88 | 86.06 | 22.94 | 67.36 | 45.73 | 45.37 | 68.21 | |
| MMA2025.12 | 71 | 66.61 | 93.8 | 90.3 | 66.13 | 72.07 | 86.12 | 25.33 | 68.17 | 46.57 | 49.24 | 68.32 | |
| MMRL + ReBaPLBase Method=MMRL2025.11 | 71 | 67.62 | 94.6 | 91.97 | 66.63 | 72.57 | 86.43 | 26.33 | 67.9 | 47.27 | 52.97 | 69.5 | |
| MaPLe2025.12 | 70.72 | 66.3 | 93.53 | 90.49 | 65.57 | 72.23 | 86.2 | 24.74 | 67.01 | 46.49 | 48.06 | 68.69 | |
| MMRL*re-run=true2025.11 | 70.13 | 66.87 | 94.3 | 91 | 66.2 | 71.53 | 86.2 | 26.07 | 67.5 | 46.93 | 49.83 | 69.13 | |
| PromptSRC*re-run=true2025.11 | 68.96 | 65.5 | 93.47 | 90.33 | 65.87 | 70.4 | 83.66 | 24.13 | 67.17 | 46.4 | 45.97 | 67.67 | |
| MaPLe + ReBaPLBase Method=MaPLe2025.11 | 68.66 | 66.77 | 93.8 | 90.73 | 66.3 | 72.57 | 86.4 | 24.37 | 67.7 | 47.17 | 50.9 | 67.87 | |
| MaPLe*re-run=true2025.11 | 67.96 | 65.63 | 93.17 | 90.2 | 65.97 | 71.07 | 86.33 | 23.23 | 67.23 | 47.2 | 45.7 | 66.27 |