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Image Classification on Average 11 datasets (Novel)

82.7Top-1 Accuracy (Avg)2SFS + ADK
62.440867.700472.9678.2196Sep 3, 2025Sep 20, 2025Oct 8, 2025Oct 26, 2025Nov 13, 2025Dec 1, 2025Dec 19, 2025

Evaluation results

MethodLinks
2SFS + ADKBackbone=ViT-L/142025.1282.7—77.783.151.675.784.495.198.485.1987585.5
CLIP-LoRA + ADKBackbone=ViT-L/142025.1281.9—78.183.549.873.783.194.79883.497.774.484.2
Rep-Adapter + ADKBackbone=ViT-L/142025.1280.4—77.381.845.274.381.994.597.38197.170.983.5
CLIPBackbone=ViT-L/142025.1280.1—7477.744.282.984.794.896.583.195.467.979.7
CLIP-LoRABackbone=ViT-L/142025.1280—77.282.242.370.683.294.698.580.297.570.982.8
2SFSBackbone=ViT-L/142025.1279.9—76.982.343.268.284.794.998.880.996.869.982.1
MMABackbone=ViT-L/142025.1279.1—76.781.842.562.883.895.198.780.29670.881.4
Rep-AdapterBackbone=ViT-L/142025.1277.8—76.579.440.663.780.894.2987796.468.380.5
CLIP-SVD2025.0976.29————————————
TCP2025.0975.36————————————
MaPLe2025.0975.14————————————
CLIP-LoRA2025.0974.8————————————
GDA2025.0974.53————————————
CLIP2025.0974.22————————————
KgCoOp2025.0973.6————————————
IVLP2025.0971.79————————————
CoCoOp2025.0971.69————————————
ProGrad2025.0970.75————————————
CoOp2025.0963.22————————————
MaPLe2023.11—75.09———————————
MaPLe+TPTtest-time prompt tuning=true2023.11—74.95———————————
ProDA2023.11—72.3———————————
PromptAlign2023.11—75.88———————————