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 551–600 of 1648 papers

TitleStatusHype
Crossing the Gap: Domain Generalization for Image Captioning—0
Cross-Image-Attention for Conditional Embeddings in Deep Metric Learning—0
Exploring Temporal Concurrency for Video-Language Representation LearningCode0
Learning with Diversity: Self-Expanded Equalization for Better Generalized Deep Metric Learning—0
HIER: Metric Learning Beyond Class Labels via Hierarchical Regularization—0
Joint Discriminative and Metric Embedding Learning for Person Re-Identification—0
NBC-Softmax : Darkweb Author fingerprinting and migration trackingCode0
Multimodal Prototype-Enhanced Network for Few-Shot Action Recognition—0
Self-training via Metric Learning for Source-Free Domain Adaptation of Semantic Segmentation—0
Efficient Malware Analysis Using Metric Embeddings—0
Few-Shot Specific Emitter Identification via Hybrid Data Augmentation and Deep Metric LearningCode0
Inconsistency Ranking-based Noisy Label Detection for High-quality DataCode0
Rogue Emitter Detection Using Hybrid Network of Denoising Autoencoder and Deep Metric Learning—0
Advancing Deep Metric Learning Through Multiple Batch Norms And Multi-Targeted Adversarial Examples—0
Learning and Understanding a Disentangled Feature Representation for Hidden Parameters in Reinforcement Learning—0
Distance Metric Learning Loss Functions in Few-Shot Scenarios of Supervised Language Models Fine-Tuning—0
Angular triangle distance for ordinal metric learning—0
Metric Learning as a Service with Covariance Embedding—0
Revisiting Distance Metric Learning for Few-Shot Natural Language Classification—0
From Forks to Forceps: A New Framework for Instance Segmentation of Surgical Instruments—0
Evaluation of the impact of the indiscernibility relation on the fuzzy-rough nearest neighbours algorithm—0
InDiReCT: Language-Guided Zero-Shot Deep Metric Learning for ImagesCode0
A Novel Center-based Deep Contrastive Metric Learning Method for the Detection of Polymicrogyria in Pediatric Brain MRICode0
Multimorbidity Content-Based Medical Image Retrieval Using Proxies—0
Self-Supervised 3D Traversability Estimation with Proxy Bank Guidance—0
Denoising Multi-Similarity Formulation: A Self-paced Curriculum-Driven Approach for Robust Metric Learning—0
Rethinking Batch Sample Relationships for Data Representation: A Batch-Graph Transformer based Approach—0
Informative Sample-Aware Proxy for Deep Metric Learning—0
Targeted Attention for Generalized- and Zero-Shot Learning—0
Probabilistic Deep Metric Learning for Hyperspectral Image ClassificationCode0
Self-distillation with Online Diffusion on Batch Manifolds Improves Deep Metric LearningCode0
Few-shot Metric Learning: Online Adaptation of Embedding for Retrieval—0
Few-Shot Character Understanding in Movies as an Assessment to Meta-Learning of Theory-of-MindCode0
Variational Quantum Kernels with Task-Specific Quantum Metric Learning—0
Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning—0
Disentangled representation learning for multilingual speaker recognition—0
On Medians of (Randomized) Pairwise Means—0
Metric Learning for User-defined Keyword Spotting—0
Zero Day Threat Detection Using Metric Learning Autoencoders—0
Gravitational Dimensionality Reduction Using Newtonian Gravity and Einstein's General RelativityCode0
A few-shot learning approach with domain adaptation for personalized real-life stress detection in close relationshipsCode0
AdaMS: Deep Metric Learning with Adaptive Margin and Adaptive Scale for Acoustic Word Discrimination—0
IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image SegmentationCode0
Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous ActionsCode0
Towards Comprehensive Representation Enhancement in Semantics-guided Self-supervised Monocular Depth Estimation—0
Dissecting Deep Metric Learning Losses for Image-Text RetrievalCode0
Mathematical Justification of Hard Negative Mining via Isometric Approximation Theorem—0
Learning Universe Model for Partial Matching Networks over Multiple Graphs—0
No Pairs Left Behind: Improving Metric Learning with Regularized Triplet Objective—0
Fine-tune your Classifier: Finding Correlations With Temperature—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