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 451–500 of 1648 papers

TitleStatusHype
Hyp-UML: Hyperbolic Image Retrieval with Uncertainty-aware Metric Learning—0
Long-Tailed Classification Based on Coarse-Grained Leading Forest and Multi-Center LossCode0
SCoRe: Submodular Combinatorial Representation Learning—0
Dark Side Augmentation: Generating Diverse Night Examples for Metric LearningCode0
Image-Text Pre-Training for Logo Recognition—0
RaLF: Flow-based Global and Metric Radar Localization in LiDAR Maps—0
Ugly Ducklings or Swans: A Tiered Quadruplet Network with Patient-Specific Mining for Improved Skin Lesion Classification—0
Learning Unified Distance Metric Across Diverse Data Distributions with Parameter-Efficient Transfer Learning—0
Getting More for Less: Using Weak Labels and AV-Mixup for Robust Audio-Visual Speaker Verification—0
Compressive Mahalanobis Metric Learning Adapts to Intrinsic Dimension—0
A Long-Tail Friendly Representation Framework for Artist and Music Similarity—0
BWSNet: Automatic Perceptual Assessment of Audio Signals—0
Bridging the Projection Gap: Overcoming Projection Bias Through Parameterized Distance Learning—0
On the Localization of Ultrasound Image Slices within Point Distribution ModelsCode0
Towards Food Image Retrieval via Generalization-oriented Sampling and Loss Function DesignCode0
Data-Side Efficiencies for Lightweight Convolutional Neural Networks—0
Open Set Synthetic Image Source Attribution—0
Generalized Sum Pooling for Metric LearningCode0
Identity-Aware Semi-Supervised Learning for Comic Character Re-Identification—0
Quantifying Outlierness of Funds from their Categories using Supervised Similarity—0
Deep Metric Learning for the Hemodynamics Inference with Electrocardiogram SignalsCode0
SpaDen : Sparse and Dense Keypoint Estimation for Real-World Chart Understanding—0
Center Contrastive Loss for Metric Learning—0
MES-Loss: Mutually equidistant separation metric learning loss function—0
Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices—0
DeepCL: Deep Change Feature Learning on Remote Sensing Images in the Metric SpaceCode0
Unifying Token and Span Level Supervisions for Few-Shot Sequence LabelingCode0
Generalizable Embeddings with Cross-batch Metric Learning—0
A metric learning approach for endoscopic kidney stone identification—0
Threshold-Consistent Margin Loss for Open-World Deep Metric Learning—0
Unsupervised Feature Learning with Emergent Data-Driven Prototypicality—0
Automatic MILP Solver Configuration By Learning Problem Similarities—0
Metric Learning-Based Timing Synchronization by Using Lightweight Neural Network—0
Improving CNN-based Person Re-identification using score Normalization—0
Mean Field Theory in Deep Metric Learning—0
A Positive-Unlabeled Metric Learning Framework for Document-Level Relation Extraction with Incomplete LabelingCode0
Improved Aircraft Environmental Impact Segmentation via Metric Learning—0
Catching Image Retrieval Generalization—0
Deep Metric Learning with Soft Orthogonal Proxies—0
Annotation Cost Efficient Active Learning for Content Based Image Retrieval—0
Renderers are Good Zero-Shot Representation Learners: Exploring Diffusion Latents for Metric LearningCode0
A Weakly Supervised Approach to Emotion-change Prediction and Improved Mood Inference—0
Contrastive Attention Networks for Attribution of Early Modern Print—0
Linear Distance Metric Learning with Noisy Labels—0
Mixed-type Distance Shrinkage and Selection for Clustering via Kernel Metric Learning—0
Class Anchor Margin Loss for Content-Based Image Retrieval—0
Counterpart Fairness -- Addressing Systematic between-group Differences in Fairness EvaluationCode0
Acquiring Frame Element Knowledge with Deep Metric Learning for Semantic Frame Induction—0
Improving the Gap in Visual Speech Recognition Between Normal and Silent Speech Based on Metric Learning—0
Learning for Transductive Threshold Calibration in Open-World Recognition—0
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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