SOTAVerified

Metric Learning

The goal of Metric Learning is to learn a representation function that maps objects into an embedded space. The distance in the embedded space should preserve the objects’ similarity — similar objects get close and dissimilar objects get far away. Various loss functions have been developed for Metric Learning. For example, the contrastive loss guides the objects from the same class to be mapped to the same point and those from different classes to be mapped to different points whose distances are larger than a margin. Triplet loss is also popular, which requires the distance between the anchor sample and the positive sample to be smaller than the distance between the anchor sample and the negative sample.

Source: Road Network Metric Learning for Estimated Time of Arrival

Papers

Showing 15511600 of 1648 papers

TitleStatusHype
Multi-Manifold Deep Metric Learning for Image Set Classification0
Learning Similarity Metrics for Dynamic Scene Segmentation0
Integrating Parametric and Non-Parametric Models For Scene Labeling0
Projection Metric Learning on Grassmann Manifold With Application to Video Based Face Recognition0
Metric Imitation by Manifold Transfer for Efficient Vision Applications0
Deep Transfer Metric Learning0
Sample complexity of learning Mahalanobis distance metrics0
Bounded-Distortion Metric Learning0
Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right FeatureCode0
Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy?0
When Face Recognition Meets with Deep Learning: an Evaluation of Convolutional Neural Networks for Face Recognition0
Unsupervised Feature Learning from Temporal Data0
Large-scale Log-determinant Computation through Stochastic Chebyshev ExpansionsCode0
Jointly Learning Multiple Measures of Similarities from Triplet Comparisons0
Online Pairwise Learning Algorithms with Kernels0
Supervised LogEuclidean Metric Learning for Symmetric Positive Definite Matrices0
Iterated Support Vector Machines for Distance Metric Learning0
Scaling-up Empirical Risk Minimization: Optimization of Incomplete U-statistics0
Riemannian Metric Learning for Symmetric Positive Definite Matrices0
Deep metric learning using Triplet networkCode1
Large Scale Distributed Distance Metric Learning0
Unsupervised Learning of Spatiotemporally Coherent Metrics0
Metric Learning Driven Multi-Task Structured Output Optimization for Robust Keypoint Tracking0
Discriminative Metric Learning by Neighborhood Gerrymandering0
Cross-Modal Similarity Learning : A Low Rank Bilinear Formulation0
Multi-Task Metric Learning on Network Data0
Similarity Learning for High-Dimensional Sparse DataCode0
Heterogeneous Metric Learning with Content-based Regularization for Software Artifact Retrieval0
Active Metric Learning from Relative Comparisons0
Metric Learning for Temporal Sequence Alignment0
A Novel Semi-Supervised Algorithm for Rare Prescription Side Effect Discovery0
Open-set Person Re-identification0
Deep Metric Learning for Practical Person Re-Identification0
Improving Performance of Self-Organising Maps with Distance Metric Learning Method0
Direct Density-Derivative Estimation and Its Application in KL-Divergence Approximation0
Person Re-identification by Local Maximal Occurrence Representation and Metric LearningCode0
Linear Dimensionality Reduction: Survey, Insights, and GeneralizationsCode0
Discriminative Deep Metric Learning for Face Verification in the Wild0
Fantope Regularization in Metric Learning0
Large-Scale Visual Font Recognition0
Multimodal Learning in Loosely-organized Web Images0
Tracklet Association with Online Target-Specific Metric Learning0
Learning Euclidean-to-Riemannian Metric for Point-to-Set Classification0
Multi-view Metric Learning for Multi-view Video Summarization0
Two-Stage Metric Learning0
Scalable Similarity Learning using Large Margin Neighborhood Embedding0
Sparse Compositional Metric Learning0
Semi-Supervised Nonlinear Distance Metric Learning via Forests of Max-Margin Cluster Hierarchies0
Fine-Grained Visual Categorization via Multi-stage Metric Learning0
Kernel-based Distance Metric Learning in the Output Space0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Unicom+ViT-L@336pxR@198.2Unverified
2Hyp-DINO 8x8R@192.8Unverified
3ResNet-50 + AVSLR@191.5Unverified
4NEDR@191.5Unverified
5ResNet-50 + Intra-Batch (ensemble of 5)R@191.5Unverified
6EfficientDML-VPTSP-G/512R@191.2Unverified
7CCL (ResNet-50)R@191.02Unverified
8ResNet50 + LanguageR@190.2Unverified
9ResNet-50 + MetrixR@189.6Unverified
10ResNet50 + S2SDR@189.5Unverified
#ModelMetricClaimedVerifiedStatus
1Unicom+ViT-L@336pxR@191.2Unverified
2STIRR@188.3Unverified
3Recall@k Surrogate Loss (ViT-B/16)R@188Unverified
4ViT-TripletR@186.5Unverified
5ROADMAP (DeiT-S)R@186Unverified
6Hyp-ViTR@185.9Unverified
7Hyp-DINOR@185.1Unverified
8Recall@k Surrogate Loss (ViT-B/32)R@185.1Unverified
9CCL (ResNet-50)R@183.1Unverified
10ROADMAP (ResNet-50)R@183.1Unverified
#ModelMetricClaimedVerifiedStatus
1Unicom+ViT-L@336pxR@190.1Unverified
2EfficientDML-VPTSP-G/512R@188.5Unverified
3Hyp-ViTR@185.6Unverified
4Hyp-DINOR@180.9Unverified
5NEDR@174.9Unverified
6CCL (ResNet-50)R@173.45Unverified
7ResNet-50 + AVSLR@171.9Unverified
8ResNet-50 + Intra-Batch ConnectionsR@171.8Unverified
9ResNet50 + LanguageR@171.4Unverified
10ResNet-50 + MetrixR@171.4Unverified
#ModelMetricClaimedVerifiedStatus
1Unicom+ViT-L@336pxR@196.7Unverified
2STIRR@195Unverified
3MGAR@194.3Unverified
4Hyp-ViTR@192.5Unverified
5Hyp-DINOR@192.4Unverified
6CCL (ResNet-50)R@192.31Unverified
7Gradient SurgeryR@192.21Unverified
8ResNet-50 + MetrixR@192.2Unverified
9EfficientDML-VPTSP-G/512R@192.1Unverified
10ViT-TripletR@192.1Unverified
#ModelMetricClaimedVerifiedStatus
1HAPPIERAverage-mAP43.8Unverified
2CSLAverage-mAP31Unverified
#ModelMetricClaimedVerifiedStatus
1HAPPIERAverage-mAP38Unverified
2CSLAverage-mAP28.7Unverified
#ModelMetricClaimedVerifiedStatus
1HAPPIERAverage-mAP37Unverified
2CSLAverage-mAP12.1Unverified