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 301–350 of 1648 papers

TitleStatusHype
Stability-based Generalization Analysis of Randomized Coordinate Descent for Pairwise Learning—0
Solving Instance Detection from an Open-World Perspective—0
GPU-accelerated Multi-relational Parallel Graph Retrieval for Web-scale Recommendations—0
A novel approach to data generation in generative model—0
Automatic Identification of Samples in Hip-Hop Music via Multi-Loss Training and an Artificial Dataset—0
Learning Fused State Representations for Control from Multi-View Observations—0
Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation ModelsCode0
Supervised Similarity for High-Yield Corporate Bonds with Quantum Cognition Machine Learning—0
Supervised Quadratic Feature Analysis: Information Geometry Approach for Dimensionality ReductionCode0
Personalized Layer Selection for Graph Neural Networks—0
Geometric Mean Improves Loss For Few-Shot Learning—0
CroMe: Multimodal Fake News Detection using Cross-Modal Tri-Transformer and Metric Learning—0
ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning—0
Unit Region Encoding: A Unified and Compact Geometry-aware Representation for Floorplan Applications—0
Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair SelectionCode0
Mutual Regression Distance—0
Metric Learning with Progressive Self-Distillation for Audio-Visual Embedding Learning—0
Cooperative Decentralized Backdoor Attacks on Vertical Federated Learning—0
Anomaly Triplet-Net: Progress Recognition Model Using Deep Metric Learning Considering Occlusion for Manual Assembly Work—0
From Age Estimation to Age-Invariant Face Recognition: Generalized Age Feature Extraction Using Order-Enhanced Contrastive Learning—0
Towards Adversarially Robust Deep Metric Learning—0
MetricDepth: Enhancing Monocular Depth Estimation with Deep Metric Learning—0
Towards understanding how attention mechanism works in deep learning—0
Towards structure-preserving quantum encodings—0
Sensitive Image Classification by Vision Transformers—0
Three Things to Know about Deep Metric Learning—0
Bringing Multimodality to Amazon Visual Search System—0
Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces—0
Image Retrieval Methods in the Dissimilarity Space—0
Multi-Level Correlation Network For Few-Shot Image ClassificationCode0
Metric-DST: Mitigating Selection Bias Through Diversity-Guided Semi-Supervised Metric LearningCode0
Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches—0
Integrating Deep Metric Learning with Coreset for Active Learning in 3D SegmentationCode0
Scalable Deep Metric Learning on Attributed Graphs—0
Outliers resistant image classification by anomaly detection—0
Fast unsupervised ground metric learning with tree-Wasserstein distance—0
Metric Learning for Tag Recommendation: Tackling Data Sparsity and Cold Start Issues—0
State Chrono Representation for Enhancing Generalization in Reinforcement LearningCode0
Metric Learning for 3D Point Clouds Using Optimal Transport—0
Few-shot Open Relation Extraction with Gaussian Prototype and Adaptive Margin—0
Learning to Generate and Evaluate Fact-checking Explanations with Transformers—0
GSSF: Generalized Structural Sparse Function for Deep Cross-modal Metric LearningCode0
PReP: Efficient context-based shape retrieval for missing parts—0
Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data ManifoldsCode0
Audio-based Kinship Verification Using Age Domain Conversion—0
GLRT-Based Metric Learning for Remote Sensing Object Retrieval—0
DAAL: Density-Aware Adaptive Line Margin Loss for Multi-Modal Deep Metric LearningCode0
SeqNet: Sequential Networks for One-Shot Traffic Sign Recognition With Transfer LearningCode0
Zero-Shot Skeleton-based Action Recognition with Dual Visual-Text Alignment—0
A Bottom-Up Approach to Class-Agnostic Image Segmentation—0
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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
3ResNet-50 + AVSLR@191.5—Unverified
4NEDR@191.5—Unverified
5ResNet-50 + Intra-Batch (ensemble of 5)R@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