Journal ArticleDOI
Discriminative Deep Metric Learning for Face and Kinship Verification
Jiwen Lu,Junlin Hu,Yap-Peng Tan +2 more
Reads0
Chats0
TLDR
A discriminative deep multi-metric learning method to jointly learn multiple neural networks, under which the correlation of different features of each sample is maximized, and the distance of each positive pair is reduced and that of each negative pair is enlarged.Abstract:
This paper presents a new discriminative deep metric learning (DDML) method for face and kinship verification in wild conditions. While metric learning has achieved reasonably good performance in face and kinship verification, most existing metric learning methods aim to learn a single Mahalanobis distance metric to maximize the inter-class variations and minimize the intra-class variations, which cannot capture the nonlinear manifold where face images usually lie on. To address this, we propose a DDML method to train a deep neural network to learn a set of hierarchical nonlinear transformations to project face pairs into the same latent feature space, under which the distance of each positive pair is reduced and that of each negative pair is enlarged. To better use the commonality of multiple feature descriptors to make all the features more robust for face and kinship verification, we develop a discriminative deep multi-metric learning method to jointly learn multiple neural networks, under which the correlation of different features of each sample is maximized, and the distance of each positive pair is reduced and that of each negative pair is enlarged. Extensive experimental results show that our proposed methods achieve the acceptable results in both face and kinship verification.read more
Citations
More filters
Journal ArticleDOI
A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection
Hao Chen,Zhenwei Shi +1 more
TL;DR: This work proposes a novel Siamese-based spatial–temporal attention neural network, which improves the F1-score of the baseline model from 83.9 to 87.3 with acceptable computational overhead and introduces a CD dataset LEVIR-CD, which is two orders of magnitude larger than other public datasets of this field.
Journal ArticleDOI
Deep Metric Learning: A Survey
Mahmut Kaya,Hasan Sakir Bilge +1 more
TL;DR: This article is considered to be important, as it is the first comprehensive study in which sampling strategy, appropriate distance metric, and the structure of the network are systematically analyzed and evaluated as a whole and supported by comparing the quantitative results of the methods.
Proceedings ArticleDOI
Deep Adversarial Metric Learning
TL;DR: This paper proposes a deep adversarial metric learning (DAML) framework to generate synthetic hard negatives from the observed negative samples, which is widely applicable to supervised deep metric learning methods.
Journal ArticleDOI
Deep Metric Learning for Visual Understanding: An Overview of Recent Advances
Jiwen Lu,Junlin Hu,Jie Zhou +2 more
TL;DR: A variety of metric learning methods have been proposed in the literature and many of them have been successfully employed in visual understanding tasks such as face recognition, image classification, and image-based geolocalization.
Book ChapterDOI
Deep Variational Metric Learning
TL;DR: This paper proposes a deep variational metric learning (DVML) framework to explicitly model the intra-class variance and disentangle the intra -class invariance, namely, the class centers, and can simultaneously generate discriminative samples to improve robustness.
References
More filters
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Proceedings ArticleDOI
Histograms of oriented gradients for human detection
Navneet Dalal,Bill Triggs +1 more
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Journal ArticleDOI
A fast learning algorithm for deep belief nets
TL;DR: A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory.