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 701–750 of 1648 papers

TitleStatusHype
Attention-based Ensemble for Deep Metric Learning—0
iFAN: Image-Instance Full Alignment Networks for Adaptive Object Detection—0
Image-based OoD-Detector Principles on Graph-based Input Data in Human Action Recognition—0
Image Patch-Matching with Graph-Based Learning in Street Scenes—0
Image Retrieval Methods in the Dissimilarity Space—0
Image-Text Pre-Training for Logo Recognition—0
FlameFinder: Illuminating Obscured Fire through Smoke with Attentive Deep Metric Learning—0
Improved Deep Metric Learning with Multi-class N-pair Loss Objective—0
Improved Diversity-Promoting Collaborative Metric Learning for Recommendation—0
Deep Metric Learning for Practical Person Re-Identification—0
Improved Hard Example Mining by Discovering Attribute-based Hard Person Identity—0
Improved Strongly Adaptive Online Learning using Coin Betting—0
A Geometric take on Metric Learning—0
Improving Calibration in Deep Metric Learning With Cross-Example Softmax—0
Improving CNN-based Person Re-identification using score Normalization—0
Deep metric learning improves lab of origin prediction of genetically engineered plasmids—0
Compressive Mahalanobis Metric Learning Adapts to Intrinsic Dimension—0
Improving Deep Metric Learning with Virtual Classes and Examples Mining—0
Improving Few-shot Learning with Weakly-supervised Object Localization—0
Deep Metric Learning Network using Proxies for Chromosome Classification in Karyotyping Test—0
Improving Image co-segmentation via Deep Metric Learning—0
Deep Similarity Metric Learning for Real-Time Pedestrian Tracking—0
Improving Performance of Self-Organising Maps with Distance Metric Learning Method—0
Fine-tune your Classifier: Finding Correlations With Temperature—0
FinePseudo: Improving Pseudo-Labelling through Temporal-Alignablity for Semi-Supervised Fine-Grained Action Recognition—0
Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classification—0
Improving the Gap in Visual Speech Recognition Between Normal and Silent Speech Based on Metric Learning—0
Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss—0
Improving Voice Trigger Detection with Metric Learning—0
Incorporating Intra-Class Variance to Fine-Grained Visual Recognition—0
Beyond the Deep Metric Learning: Enhance the Cross-Modal Matching with Adversarial Discriminative Domain Regularization—0
Incremental Class Learning using Variational Autoencoders with Similarity Learning—0
Large Scale Distributed Distance Metric Learning—0
Learning Rates for Nonconvex Pairwise Learning—0
Large Scale Local Online Similarity/Distance Learning Framework based on Passive/Aggressive—0
Inductive Regularized Learning of Kernel Functions—0
Information Theoretic Learning with Infinitely Divisible Kernels—0
Informative Sample-Aware Proxy for Deep Metric Learning—0
Bidirectional Feature Globalization for Few-shot Semantic Segmentation of 3D Point Cloud Scenes—0
Instance Cross Entropy for Deep Metric Learning—0
3D Facial Matching by Spiral Convolutional Metric Learning and a Biometric Fusion-Net of Demographic Properties—0
Large-Scale Visual Font Recognition—0
CLAREL: Classification via retrieval loss for zero-shot learning—0
Intelligent Known and Novel Aircraft Recognition -- A Shift from Classification to Similarity Learning for Combat Identification—0
Interpolated Discretized Embedding of Single Vectors and Vector Pairs for Classification, Metric Learning and Distance Approximation—0
Fine-Grained Visual Categorization via Multi-stage Metric Learning—0
Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting—0
Interpretable Distance Metric Learning for Handwritten Chinese Character Recognition—0
Interpretable embeddings to understand computing careers—0
Cross Vision-RF Gait Re-identification with Low-cost RGB-D Cameras and mmWave Radars—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