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SotA/Methodology/Metric Learning/In-Shop

Metric Learning on In-Shop

Metric: R@1 (higher is better)

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#Model↕R@1▼AugmentationsPaperDate↕Code
1Unicom+ViT-L@336px96.7YesUnicom: Universal and Compact Representation Lea...2023-04-12Code
2STIR95NoSTIR: Siamese Transformer for Image Retrieval Po...2023-04-26Code
3MGA94.3NoFashion Image Retrieval with Multi-Granular Alig...2023-02-16-
4Hyp-ViT92.5YesHyperbolic Vision Transformers: Combining Improv...2022-03-21Code
5Hyp-DINO92.4NoHyperbolic Vision Transformers: Combining Improv...2022-03-21Code
6CCL (ResNet-50)92.31NoCenter Contrastive Loss for Metric Learning2023-08-01-
7Gradient Surgery92.21NoDissecting the impact of different loss function...2022-01-27-
8ResNet-50 + Metrix92.2YesIt Takes Two to Tango: Mixup for Deep Metric Lea...2021-06-09Code
9ViT-Triplet92.1NoSTIR: Siamese Transformer for Image Retrieval Po...2023-04-26Code
10EfficientDML-VPTSP-G/51292.1YesLearning Semantic Proxies from Visual Prompts fo...2024-02-04Code
11NED91.3YesCalibrated neighborhood aware confidence measure...2020-06-08-
12ResNet-50 + ProxyNCA++90.9YesProxyNCA++: Revisiting and Revitalizing Proxy Ne...2020-04-02Code
13ResNet-50 + Cross-Entropy90.6YesA unifying mutual information view of metric lea...2020-03-19Code
14SCT(512)90NoHard negative examples are hard, but useful2020-07-24Code
15EPSHN(512)87.8NoImproved Embeddings with Easy Positive Triplet M...2019-04-08Code