Image Classification on ImageNet-S (test)
59.07Top-1 AccSS-CA
Evaluation Results
| Method | Links | |
|---|---|---|
| SS-CAModel=CLIP (ViT /32b)2025.11 | 59.07 | |
| Chen et al.Model=CLIP (ViT /32b)2025.11 | 58.18 | |
| Xiao et al.Model=CLIP (ViT /32b)2025.11 | 58.1 | |
| Conventional TrainingModel=CLIP (ViT /32b)2025.11 | 57.56 | |
| GPUAEvaluation Protocol=Zero-shot2026.06 | 56.9 | |
| SS-CABackbone=CLIP (ViT/32b), Evaluation Protocol=Linear Probing2025.11 | 55.77 | |
| Chen et al.Backbone=CLIP (ViT/32b), Evaluation Protocol=Linear Probing2025.11 | 55.12 | |
| Conventional TrainingBackbone=CLIP (ViT/32b), Evaluation Protocol=Linear Probing2025.11 | 54.99 | |
| Xiao et al.Backbone=CLIP (ViT/32b), Evaluation Protocol=Linear Probing2025.11 | 54.78 | |
| TACO2026.06 | 50.52 | |
| WISE-FT2026.06 | 49.72 | |
| SS-CAModel=ViT2025.11 | 49.71 | |
| PromptSRC2026.06 | 49.55 | |
| MMRL2026.06 | 49.17 | |
| MaPLe2026.06 | 49.15 | |
| MMA2026.06 | 49.13 | |
| CoCoOp2026.06 | 48.75 | |
| PromptSRC*2025.11 | 48.6 | |
| MaPLe + ReBaPL2025.11 | 48.5 | |
| MMRL + ReBaPL2025.11 | 48.4 | |
| Chen et al.Model=ViT2025.11 | 48.14 | |
| Xiao et al.Model=ViT2025.11 | 48.12 | |
| Conventional TrainingModel=ViT2025.11 | 47.81 | |
| MMRL*2025.11 | 47.8 | |
| MaPLe*2025.11 | 47.7 | |
| SS-CAModel=ResNet-1012025.11 | 46.45 | |
| CLIPEvaluation Protocol=Zero-shot2026.06 | 46.1 | |
| Chen et al.Model=ResNet-1012025.11 | 45.99 | |
| Xiao et al.Model=ResNet-1012025.11 | 45.94 | |
| Conventional TrainingModel=ResNet-1012025.11 | 45.76 | |
| LaFTerBackbone=ViT-B/322023.05 | 42.7 | |
| UPLBackbone=ViT-B/322023.05 | 42.4 | |
| TRUST2025.09 | 41.5 | |
| TRUST naive2025.09 | 41.1 | |
| CLIPBackbone=ViT-B/322023.05 | 40.6 | |
| CLIP-PRBackbone=ViT-B/322023.05 | 38.6 | |
| SAR2025.09 | 32.6 | |
| SHOT2025.09 | 32.6 | |
| Tent2025.09 | 32.5 | |
| SS-CABackbone=ViT, Evaluation Protocol=End-to-End Fine-Tuning2025.11 | 31.56 | |
| Source only2025.09 | 31.4 | |
| ETA2025.09 | 31.4 | |
| LAME2025.09 | 31.4 | |
| Chen et al.Backbone=ViT, Evaluation Protocol=End-to-End Fine-Tuning2025.11 | 31.05 | |
| Xiao et al.Backbone=ViT, Evaluation Protocol=End-to-End Fine-Tuning2025.11 | 30.92 | |
| Conventional TrainingBackbone=ViT, Evaluation Protocol=End-to-End Fine-Tuning2025.11 | 30.9 | |
| SS-CABackbone=ResNet-101, Evaluation Protocol=End-to-End Fine-Tuning2025.11 | 12.15 | |
| Chen et al.Backbone=ResNet-101, Evaluation Protocol=End-to-End Fine-Tuning2025.11 | 11.54 | |
| Conventional TrainingBackbone=ResNet-101, Evaluation Protocol=End-to-End Fine-Tuning2025.11 | 11.34 | |
| Xiao et al.Backbone=ResNet-101, Evaluation Protocol=End-to-End Fine-Tuning2025.11 | 11.34 |