Metric Learning
423 papers with code • 5 benchmarks • 26 datasets
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
Benchmarks
These leaderboards are used to track progress in Metric Learning
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Libraries
Use these libraries to find Metric Learning models and implementationsDatasets
CUB-200-2011
CASIA-WebFace
Stanford Online Products
VIPeR
In-Shop
Structured3D
VeRi-Wild
WildDeepfake
Google Landmarks Dataset v2
COMETA
Most implemented papers
In Defense of the Triplet Loss for Person Re-Identification
layumi/Person_reID_baseline_pytorch
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In the past few years, the field of computer vision has gone through a revolution fueled mainly by the advent of large datasets and the adoption of deep convolutional neural networks for end-to-end learning.
Matching Networks for One Shot Learning
oscarknagg/few-shot
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NeurIPS 2016
Our algorithm improves one-shot accuracy on ImageNet from 87. 6% to 93. 2% and from 88. 0% to 93. 8% on Omniglot compared to competing approaches.
Additive Margin Softmax for Face Verification
In this work, we introduce a novel additive angular margin for the Softmax loss, which is intuitively appealing and more interpretable than the existing works.
Circle Loss: A Unified Perspective of Pair Similarity Optimization
layumi/Person_reID_baseline_pytorch
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CVPR 2020
This paper provides a pair similarity optimization viewpoint on deep feature learning, aiming to maximize the within-class similarity $s_p$ and minimize the between-class similarity $s_n$.
Semantic Instance Segmentation with a Discriminative Loss Function
Wizaron/instance-segmentation-pytorch
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In this work we propose to tackle the problem with a discriminative loss function, operating at the pixel level, that encourages a convolutional network to produce a representation of the image that can easily be clustered into instances with a simple post-processing step.
Revisiting Training Strategies and Generalization Performance in Deep Metric Learning
Confusezius/Deep-Metric-Learning-Baselines
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ICML 2020
Deep Metric Learning (DML) is arguably one of the most influential lines of research for learning visual similarities with many proposed approaches every year.
Sampling Matters in Deep Embedding Learning
CompVis/metric-learning-divide-and-conquer
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ICCV 2017
In addition, we show that a simple margin based loss is sufficient to outperform all other loss functions.
Batch DropBlock Network for Person Re-identification and Beyond
daizuozhuo/batch-feature-erasing-network
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ICCV 2019
In this paper, we propose the Batch DropBlock (BDB) Network which is a two branch network composed of a conventional ResNet-50 as the global branch and a feature dropping branch.
Deep Cosine Metric Learning for Person Re-Identification
nwojke/cosine_metric_learning
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Metric learning aims to construct an embedding where two extracted features corresponding to the same identity are likely to be closer than features from different identities.
PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment
In this paper, we tackle the challenging few-shot segmentation problem from a metric learning perspective and present PANet, a novel prototype alignment network to better utilize the information of the support set.
