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Few-shot Image Classification on StanfordCars (test)

81.29Accuracy (1-shot)C2-Net
32.233244.969157.70570.4409Mar 11, 2025Apr 28, 2025Jun 15, 2025Aug 2, 2025Sep 19, 2025Nov 6, 2025Dec 25, 2025

Evaluation results

MethodLinks
C2-NetBackbone=Conv-4, Published In=AAAI 20242025.1281.29————91.08
CausalFSFGBackbone=Conv-42025.1279.96————93.07
Bi-FRNBackbone=Conv-4, Published In=AAAI 20232025.1275.74————91.58
PromptSRC2025.0369.473.477.1380.9783.83—
PromptSRC2025.0569.473.477.1380.9783.83—
MMRL2025.0368.772.9378.1782.5786.43—
MMRL2025.0568.772.9378.1782.5786.43—
MMRL++2025.0568.272.777.8382.486.2—
MMA2025.0367.8771.7776.581.485.7—
MMA2025.0567.8771.7776.581.485.7—
FRNBackbone=Conv-4, Published In=CVPR 20212025.1267.48————87.97
CoOp2025.0367.4370.574.4779.383.07—
CoOp2025.0567.4370.574.4779.383.07—
CoCoOp2025.0367.2268.3769.3970.4471.57—
CoCoOp2025.0567.2268.3769.3970.4471.57—
MaPLe2025.0366.671.675.379.4783.57—
MaPLe2025.0566.671.675.379.4783.57—
CTXBackbone=Conv-4, Published In=NeurIPS 20202025.1266.35————82.25
PARNBackbone=Conv-4, Published In=ICCV 20192025.1266.01————73.74
FRN+TDMBackbone=Conv-4, Published In=CVPR 20222025.1265.67————86.44
DeepEMDBackbone=Conv-4, Published In=CVPR 20202025.1261.63————72.95
SAMLBackbone=Conv-4, Published In=ICCV 20192025.1261.07————88.73
LRPABNBackbone=Conv-4, Published In=TMM 20212025.1260.28————73.29
ProtoNetBackbone=Conv-4, Published In=NeurIPS 20172025.1250.88————74.89
RelationBackbone=Conv-4, Published In=CVPR 20182025.1246.04————68.52
BSNet(D&C)Backbone=Conv-4, Published In=TIP 20212025.1240.89————86.88
Linear probe CLIP2025.0335.6650.2863.3873.6780.44—
Linear probe CLIP2025.0535.6650.2863.3873.6780.44—
DN4Backbone=Conv-4, Published In=CVPR 20192025.1234.12————87.47