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 15011550 of 1648 papers

TitleStatusHype
Distance Metric Learning for Aspect Phrase Grouping0
An Enhanced Deep Feature Representation for Person Re-identification0
Scene Parsing with Integration of Parametric and Non-parametric Models0
M^2S-Net: Multi-Modal Similarity Metric Learning based Deep Convolutional Network for Answer SelectionCode0
Mahalanobis Distance Metric Learning Algorithm for Instance-based Data Stream Classification0
DARI: Distance metric And Representation Integration for Person Verification0
CP-mtML: Coupled Projection multi-task Metric Learning for Large Scale Face Retrieval0
Bayesian Neighbourhood Component Analysis0
Online Open World Recognition0
Families in the Wild (FIW): Large-Scale Kinship Image Database and Benchmarks0
Modeling Time Series Similarity with Siamese Recurrent Networks0
Template Adaptation for Face Verification and Identification0
Nonstationary Distance Metric Learning0
Learning a Discriminative Null Space for Person Re-identification0
Scalable Metric Learning via Weighted Approximate Rank Component Analysis0
Stochastic Dykstra Algorithms for Metric Learning on Positive Semi-Definite Cone0
Latent Variable Modeling with Diversity-Inducing Mutual Angular Regularization0
Beyond Classification: Latent User Interests Profiling from Visual Contents Analysis0
Fine-grained Categorization and Dataset Bootstrapping using Deep Metric Learning with Humans in the Loop0
Boosted Sparse Non-linear Distance Metric Learning0
Similarity Learning via Adaptive Regression and Its Application to Image Retrieval0
Efficient PSD Constrained Asymmetric Metric Learning for Person Re-Identification0
Regressive Virtual Metric Learning0
A Supervised Low-Rank Method for Learning Invariant Subspaces0
Semantic Video Entity Linking Based on Visual Content and Metadata0
Person Re-Identification With Discriminatively Trained Viewpoint Invariant Dictionaries0
Person Re-Identification Ranking Optimisation by Discriminant Context Information Analysis0
SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk0
Hierarchical Invariant Feature Learning with Marginalization for Person Re-Identification0
Structured learning of metric ensembles with application to person re-identification0
Constrained Deep Metric Learning for Person Re-identification0
Tracklet Association by Online Target-Specific Metric Learning and Coherent Dynamics Estimation0
Fast Metric Learning For Deep Neural Networks0
Deep Metric Learning via Lifted Structured Feature EmbeddingCode0
Learning Deep Structure-Preserving Image-Text Embeddings0
Metric learning approach for graph-based label propagation0
Nonlinear Local Metric Learning for Person Re-identification0
Adaptive Affinity Matrix for Unsupervised Metric Learning0
Max-margin Metric Learning for Speaker Recognition0
Scalable Nonlinear Embeddings for Semantic Category-based Image Retrieval0
TransA: An Adaptive Approach for Knowledge Graph Embedding0
Geometry-aware Deep Transform0
Towards Making High Dimensional Distance Metric Learning Practical0
Nonlinear Metric Learning for kNN and SVMs through Geometric Transformations0
Sparse Multidimensional Patient Modeling using Auxiliary Confidence Labels0
Online Metric-Weighted Linear Representations for Robust Visual Tracking0
Entity Hierarchy Embedding0
Targeting Ultimate Accuracy: Face Recognition via Deep Embedding0
Bayesian representation learning with oracle constraints0
Deep Transfer Metric Learning0
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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