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Metric Learning On Cub 200 2011

Metrics

R@1

Results

Performance results of various models on this benchmark

Paper Title
Unicom+ViT-L@336px90.1Unicom: Universal and Compact Representation Learning for Image Retrieval
EfficientDML-VPTSP-G/51288.5Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric Learning
Hyp-ViT85.6Hyperbolic Vision Transformers: Combining Improvements in Metric Learning
NED74.9Calibrated neighborhood aware confidence measure for deep metric learning
CCL (ResNet-50)73.45Center Contrastive Loss for Metric Learning
ResNet-50 + AVSL71.9Attributable Visual Similarity Learning
ResNet-50 + Intra-Batch Connections71.8Learning Intra-Batch Connections for Deep Metric Learning
ResNet50 + Language71.4Integrating Language Guidance into Vision-based Deep Metric Learning
ResNet-50 + Metrix71.4It Takes Two to Tango: Mixup for Deep Metric Learning
BN-Inception + Proxy-Anchor71.1Proxy Anchor Loss for Deep Metric Learning
ResNet50 + NIR70.5Non-isotropy Regularization for Proxy-based Deep Metric Learning
ResNet50 + S2SD70.1S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning
ResNet-50 + Cross-Entropy69.2A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses
ResNet50 + DiVA69.2DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning
ResNet-50 + ProxyNCA++69.0ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis
MS + DIML68.15Towards Interpretable Deep Metric Learning with Structural Matching
ResNet50 (128) + PADS67.3PADS: Policy-Adapted Sampling for Visual Similarity Learning
ABE + HORDE66.8Metric Learning With HORDE: High-Order Regularizer for Deep Embeddings
ResNet50 (128) + MIC66.1MIC: Mining Interclass Characteristics for Improved Metric Learning
BN-Inception + Group Loss65.5The Group Loss for Deep Metric Learning
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Metric Learning On Cub 200 2011 | SOTA | HyperAI