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SOTA
Metric Learning
Metric Learning On In Shop 1
Metric Learning On In Shop 1
评估指标
R@1
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
R@1
Paper Title
Repository
Hyp-DINO
92.4
Hyperbolic Vision Transformers: Combining Improvements in Metric Learning
NED
91.3
Calibrated neighborhood aware confidence measure for deep metric learning
-
EfficientDML-VPTSP-G/512
92.1
Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric Learning
STIR
95
STIR: Siamese Transformer for Image Retrieval Postprocessing
Hyp-ViT
92.5
Hyperbolic Vision Transformers: Combining Improvements in Metric Learning
ResNet-50 + Metrix
92.2
It Takes Two to Tango: Mixup for Deep Metric Learning
SCT(512)
90
Hard negative examples are hard, but useful
EPSHN(512)
87.8
Improved Embeddings with Easy Positive Triplet Mining
ViT-Triplet
92.1
STIR: Siamese Transformer for Image Retrieval Postprocessing
Unicom+ViT-L@336px
96.7
Unicom: Universal and Compact Representation Learning for Image Retrieval
ResNet-50 + Cross-Entropy
90.6
A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses
CCL (ResNet-50)
92.31
Center Contrastive Loss for Metric Learning
-
Gradient Surgery
92.21
Dissecting the impact of different loss functions with gradient surgery
-
MGA
94.3
Fashion Image Retrieval with Multi-Granular Alignment
-
ResNet-50 + ProxyNCA++
90.9
ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis
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