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 10511100 of 1648 papers

TitleStatusHype
Classifying All Interacting Pairs in a Single Shot0
Distance Metric Learning with Joint Representation DiversificationCode0
Ranking and Classification driven Feature Learning for Person Re_identificationCode0
A Simple and Effective Framework for Pairwise Deep Metric LearningCode0
Deep Iterative and Adaptive Learning for Graph Neural NetworksCode1
A Probabilistic approach for Learning Embeddings without Supervision0
Cross-Batch Memory for Embedding LearningCode1
Associative Alignment for Few-shot Image ClassificationCode0
Scalable Fine-grained Generated Image Classification Based on Deep Metric Learning0
WCE Polyp Detection with Triplet based Embeddings0
Expert-guided Regularization via Distance Metric Learning0
Deep Distributional Sequence Embeddings Based on a Wasserstein Loss0
Efficient feature embedding of 3D brain MRI images for content-based image retrieval with deep metric learning0
The Group Loss for Deep Metric LearningCode0
Curvilinear Distance Metric LearningCode0
R2D2: Reliable and Repeatable Detector and DescriptorCode0
Unbiased Evaluation of Deep Metric Learning AlgorithmsCode0
A Discriminative Learned CNN Embedding for Remote Sensing Image Scene Classification0
Adaptive Nearest Neighbor: A General Framework for Distance Metric Learning0
Instance Cross Entropy for Deep Metric Learning0
Large Scale Open-Set Deep Logo DetectionCode0
Distribution Context Aware Loss for Person Re-identification0
Unsupervised Deep Metric Learning via Auxiliary Rotation Loss0
Semantic Granularity Metric Learning for Visual Search0
Variable Star Classification Using Multi-View Metric Learning0
Part-based Multi-stream Model for Vehicle Searching0
Ground Metric Learning on GraphsCode0
DCA: Diversified Co-Attention towards Informative Live Video Commenting0
Metric Learning for Dynamic Text ClassificationCode0
Deep Metric Learning-Based Feature Embedding for Hyperspectral Image ClassificationCode0
Metric Learning with Background Noise Class for Few-shot Detection of Rare Sound Events0
Domain Generalization via Model-Agnostic Learning of Semantic FeaturesCode0
Fully Convolutional Geometric FeaturesCode1
MLAT: Metric Learning for kNN in Streaming Time Series0
An Empirical Study on Learning Fairness Metrics for COMPAS Data with Human SupervisionCode0
Collaborative Preference Embedding against Sparse Labels0
Designovel's system description for Fashion-IQ challenge 20190
Learning Hierarchical Feature Space Using CLAss-specific Subspace Multiple Kernel -- Metric Learning for Classification0
Adversarial Skill Networks: Unsupervised Robot Skill Learning from VideoCode0
SegSort: Segmentation by Discriminative Sorting of SegmentsCode0
Learning Invariant Representations of Social Media UsersCode1
A Semi-Supervised Maximum Margin Metric Learning Approach for Small Scale Person Re-identification0
Vehicle Re-identification with Viewpoint-aware Metric Learning0
Multiple Kernel Fisher Discriminant Metric Learning for Person Re-identification0
Private Protocols for U-Statistics in the Local Model and Beyond0
Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning0
Learning Point Embeddings from Shape Repositories for Few-Shot Segmentation0
Collect and Select: Semantic Alignment Metric Learning for Few-Shot LearningCode0
Deep Meta Metric LearningCode0
Deep Metric Learning With Tuplet Margin Loss0
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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