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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 601–650 of 1648 papers

TitleStatusHype
Hyperparameter-Free Out-of-Distribution Detection Using Softmax of Scaled Cosine SimilarityCode0
Learning Transferable Reward for Query Object Localization with Policy AdaptationCode0
Inconsistency Ranking-based Noisy Label Detection for High-quality DataCode0
Safe Triplet Screening for Distance Metric LearningCode0
Entity Hierarchy Embedding—0
Enhancing Understanding Through Wildlife Re-Identification—0
Context-Aware Siamese Networks for Efficient Emotion Recognition in Conversation—0
A Retrofitting Model for Incorporating Semantic Relations into Word Embeddings—0
Enhancing Remote Sensing Image Retrieval with Triplet Deep Metric Learning Network—0
Enhancing mTBI Diagnosis with Residual Triplet Convolutional Neural Network Using 3D CT—0
Enhancing Martian Terrain Recognition with Deep Constrained Clustering—0
Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces—0
Relaxed N-Pairs Loss for Context-Aware Recommendations of Television Content—0
Are encoders able to learn landmarkers for warm-starting of Hyperparameter Optimization?—0
Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches—0
Learning Test-time Augmentation for Content-based Image Retrieval—0
Content-Based Brain Tumor Retrieval for MR Images Using Transfer Learning—0
Construct Informative Triplet with Two-stage Hard-sample Generation—0
A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization—0
Energy Confused Adversarial Metric Learning for Zero-Shot Image Retrieval and Clustering—0
Constraint Selection in Metric Learning—0
End-to-end losses based on speaker basis vectors and all-speaker hard negative mining for speaker verification—0
End-to-end learning of keypoint detector and descriptor for pose invariant 3D matching—0
Constrained Deep Metric Learning for Person Re-identification—0
A Probabilistic approach for Learning Embeddings without Supervision—0
Adversarial Metric Learning—0
Active Learning based on Data Uncertainty and Model Sensitivity—0
End-to-End Illuminant Estimation Based on Deep Metric Learning—0
End-to-End Data Visualization by Metric Learning and Coordinate Transformation—0
Constellation Loss: Improving the efficiency of deep metric learning loss functions for optimal embedding—0
Exploiting Global Camera Network Constraints for Unsupervised Video Person Re-identification—0
EmbNum: Semantic labeling for numerical values with deep metric learning—0
ConceptBeam: Concept Driven Target Speech Extraction—0
Embedding Transfer via Smooth Contrastive Loss—0
Computer Vision and Metrics Learning for Hypothesis Testing: An Application of Q-Q Plot for Normality Test—0
A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives—0
Embeddings and Representation Learning for Structured Data—0
Efficient PSD Constrained Asymmetric Metric Learning for Person Re-Identification—0
Efficient Online Local Metric Adaptation via Negative Samples for Person Re-Identification—0
Compare learning: bi-attention network for few-shot learning—0
Efficient Multiple Instance Metric Learning Using Weakly Supervised Data—0
Efficient Malware Analysis Using Metric Embeddings—0
Communication/Computation Tradeoffs in Consensus-Based Distributed Optimization—0
APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic Segmentation—0
Action Quality Assessment using Siamese Network-Based Deep Metric Learning—0
3D Point Cloud Enhancement using Graph-Modelled Multiview Depth Measurements—0
Efficient feature embedding of 3D brain MRI images for content-based image retrieval with deep metric learning—0
Creating Ensembles of Classifiers through UMDA for Aerial Scene Classification—0
Efficient Distance Metric Learning by Adaptive Sampling and Mini-Batch Stochastic Gradient Descent (SGD)—0
A Novel User Representation Paradigm for Making Personalized Candidate Retrieval—0
Show:102550
← PrevPage 13 of 33Next →
All datasetsCARS196Stanford Online ProductsCUB-200-2011In-ShopDyML-AnimalDyML-ProductDyML-Vehicle

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Unicom+ViT-L@336pxR@198.2—Unverified
2Hyp-DINO 8x8R@192.8—Unverified
3NEDR@191.5—Unverified
4ResNet-50 + Intra-Batch (ensemble of 5)R@191.5—Unverified
5ResNet-50 + AVSLR@191.5—Unverified
6EfficientDML-VPTSP-G/512R@191.2—Unverified
7CCL (ResNet-50)R@191.02—Unverified
8ResNet50 + LanguageR@190.2—Unverified
9ResNet-50 + MetrixR@189.6—Unverified
10ResNet50 + S2SDR@189.5—Unverified
#ModelMetricClaimedVerifiedStatus
1Unicom+ViT-L@336pxR@191.2—Unverified
2STIRR@188.3—Unverified
3Recall@k Surrogate Loss (ViT-B/16)R@188—Unverified
4ViT-TripletR@186.5—Unverified
5ROADMAP (DeiT-S)R@186—Unverified
6Hyp-ViTR@185.9—Unverified
7Hyp-DINOR@185.1—Unverified
8Recall@k Surrogate Loss (ViT-B/32)R@185.1—Unverified
9CCL (ResNet-50)R@183.1—Unverified
10ROADMAP (ResNet-50)R@183.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Unicom+ViT-L@336pxR@190.1—Unverified
2EfficientDML-VPTSP-G/512R@188.5—Unverified
3Hyp-ViTR@185.6—Unverified
4Hyp-DINOR@180.9—Unverified
5NEDR@174.9—Unverified
6CCL (ResNet-50)R@173.45—Unverified
7ResNet-50 + AVSLR@171.9—Unverified
8ResNet-50 + Intra-Batch ConnectionsR@171.8—Unverified
9ResNet50 + LanguageR@171.4—Unverified
10ResNet-50 + MetrixR@171.4—Unverified
#ModelMetricClaimedVerifiedStatus
1Unicom+ViT-L@336pxR@196.7—Unverified
2STIRR@195—Unverified
3MGAR@194.3—Unverified
4Hyp-ViTR@192.5—Unverified
5Hyp-DINOR@192.4—Unverified
6CCL (ResNet-50)R@192.31—Unverified
7Gradient SurgeryR@192.21—Unverified
8ResNet-50 + MetrixR@192.2—Unverified
9EfficientDML-VPTSP-G/512R@192.1—Unverified
10ViT-TripletR@192.1—Unverified
#ModelMetricClaimedVerifiedStatus
1HAPPIERAverage-mAP43.8—Unverified
2CSLAverage-mAP31—Unverified
#ModelMetricClaimedVerifiedStatus
1HAPPIERAverage-mAP38—Unverified
2CSLAverage-mAP28.7—Unverified
#ModelMetricClaimedVerifiedStatus
1HAPPIERAverage-mAP37—Unverified
2CSLAverage-mAP12.1—Unverified