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ResearchFormerly WizWand

Few-shot Image Classification on StanfordCars

0.7996AccuracyuDTW
0.0113840.2160170.420650.625283Mar 23, 2023Oct 2, 2023Apr 12, 2024Oct 22, 2024May 3, 2025Nov 12, 2025May 24, 2026
Updated 5mo ago

Evaluation results

MethodLinks
uDTWWay=5, Shots=202026.050.7996——
sDTWWay=5, Shots=202026.050.7979——
PromptSRCPrompting Strategy=Prompt learning without language priors2024.100.7827——
AAPEPrompting Strategy=LLM-based2024.100.7751——
Full Fine-tuningShots=162023.040.7691——
CoOpShots=4, Prompt type=tp2023.030.7273——
KgCoOpShots=4, Prompt type=tp2023.030.7198——
ProGradShots=4, Prompt type=tp2023.030.7175——
uDTWWay=5, Shots=52026.050.6956——
CoCoOpShots=4, Prompt type=vp+tp2023.030.691——
EuclideanWay=5, Shots=202026.050.687——
sDTWWay=5, Shots=52026.050.6531——
PVPShots=162023.040.6109——
EuclideanWay=5, Shots=52026.050.5755——
PsCoWay=5, Shots=202026.050.575——
VPTShots=162023.040.5733——
Meta-SVEBMWay=5, Shots=202026.050.4623——
PsCoWay=5, Shots=52026.050.4401——
Full Fine-tuningShots=82023.040.412——
PVPShots=82023.040.4057——
Meta-GMVAEWay=5, Shots=202026.050.3836——
VPTShots=82023.040.3631——
Meta-SVEBMWay=5, Shots=52026.050.3427——
Meta-GMVAEWay=5, Shots=52026.050.3139——
PVPShots=42023.040.2608——
Full Fine-tuningShots=42023.040.2357——
VPTShots=42023.040.2062——
PVPShots=22023.040.1473——
PVPShots=12023.040.1073——
VPTShots=22023.040.0864——
Full Fine-tuningShots=22023.040.0585——
VPTShots=12023.040.052——
Full Fine-tuningShots=12023.040.0417——
BiFRNBackbone=ResNet-12, Ways=5-way2025.06—0.90440.9749
BSFABackbone=ResNet-12, Ways=5-way2025.06—0.88930.952
C2-NetBackbone=ResNet-12, Ways=5-way2025.06—0.88960.9516
CTXBackbone=ResNet-12, Ways=5-way2025.06—0.85030.9263
DeepBDCBackbone=ResNet-12, Ways=5-way2025.06—0.82280.9351
DeepEMDBackbone=ResNet-12, Ways=5-way2025.06—0.80620.9263
ESPTBackbone=ResNet-12, Ways=5-way2025.06—0.8820.9696
FRNBackbone=ResNet-12, Ways=5-way2025.06—0.88010.9575
FRN+TDMBackbone=ResNet-12, Ways=5-way2025.06—0.8880.9702
HelixFormerBackbone=ResNet-12, Ways=5-way2025.06—0.7940.9226
HMDRNBackbone=ResNet-12, Ways=5-way2025.06—0.90920.9756
LCCRNBackbone=ResNet-12, Ways=5-way2025.06—0.87040.9619
MCL-KatzBackbone=ResNet-12, Ways=5-way2025.06—0.84420.9332
MCL-Katz+PSMBackbone=ResNet-12, Ways=5-way2025.06—0.84720.9352
ProtoNetBackbone=ResNet-12, Ways=5-way2025.06—0.85460.9508
RENetBackbone=ResNet-12, Ways=5-way2025.06—0.85230.9416
SRNetBackbone=ResNet-12, Ways=5-way2025.06—0.84040.9398