scholarly journals A Local-to-Global Metric Learning Framework From the Geometric Insight

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 16953-16964 ◽  
Author(s):  
Yaxin Peng ◽  
Nijing Zhang ◽  
Ying Li ◽  
Shihui Ying
Author(s):  
Lixin Fan ◽  
Kam Woh Ng ◽  
Ce Ju ◽  
Tianyu Zhang ◽  
Chee Seng Chan

This paper proposes a novel deep polarized network (DPN) for learning to hash, in which each channel in the network outputs is pushed far away from zero by employing a differentiable bit-wise hinge-like loss which is dubbed as polarization loss. Reformulated within a generic Hamming Distance Metric Learning framework [Norouzi et al., 2012], the proposed polarization loss bypasses the requirement to prepare pairwise labels for (dis-)similar items and, yet, the proposed loss strictly bounds from above the pairwise Hamming Distance based losses. The intrinsic connection between pairwise and pointwise label information, as disclosed in this paper, brings about the following methodological improvements: (a) we may directly employ the proposed differentiable polarization loss with no large deviations incurred from the target Hamming distance based loss; and (b) the subtask of assigning binary codes becomes extremely simple --- even random codes assigned to each class suffice to result in state-of-the-art performances, as demonstrated in CIFAR10, NUS-WIDE and ImageNet100 datasets.


Author(s):  
Guixia Fu ◽  
Guofeng Zou ◽  
Mingliang Gao ◽  
Zhenzhou Wang ◽  
Zheng Liu

2019 ◽  
Vol 491 (3) ◽  
pp. 3805-3819 ◽  
Author(s):  
K B Johnston ◽  
S M Caballero-Nieves ◽  
V Petit ◽  
A M Peter ◽  
R Haber

ABSTRACT Comprehensive observations of variable stars can include time domain photometry in a multitude of filters, spectroscopy, estimates of colour (e.g. U-B), etc. When the objective is to classify variable stars, traditional machine learning techniques distill these various representations (or views) into a single feature vector and attempt to discriminate among desired categories. In this work, we propose an alternative approach that inherently leverages multiple views of the same variable star. Our multiview metric learning framework enables robust characterization of star categories by directly learning to discriminate in a multifaceted feature space, thus, eliminating the need to combine feature representations prior to fitting the machine learning model. We also demonstrate how to extend standard multiview learning, which employs multiple vectorized views, to the matrix-variate case which allows very novel variable star signature representations. The performance of our proposed methods is evaluated on the UCR Starlight and LINEAR data sets. Both the vector and matrix-variate versions of our multiview learning framework perform favourably – demonstrating the ability to discriminate variable star categories.


2020 ◽  
Vol 34 (07) ◽  
pp. 10818-10825 ◽  
Author(s):  
Yu Gao ◽  
Xintong Han ◽  
Xun Wang ◽  
Weilin Huang ◽  
Matthew Scott

Fine-grained image categorization is challenging due to the subtle inter-class differences. We posit that exploiting the rich relationships between channels can help capture such differences since different channels correspond to different semantics. In this paper, we propose a channel interaction network (CIN), which models the channel-wise interplay both within an image and across images. For a single image, a self-channel interaction (SCI) module is proposed to explore channel-wise correlation within the image. This allows the model to learn the complementary features from the correlated channels, yielding stronger fine-grained features. Furthermore, given an image pair, we introduce a contrastive channel interaction (CCI) module to model the cross-sample channel interaction with a metric learning framework, allowing the CIN to distinguish the subtle visual differences between images. Our model can be trained efficiently in an end-to-end fashion without the need of multi-stage training and testing. Finally, comprehensive experiments are conducted on three publicly available benchmarks, where the proposed method consistently outperforms the state-of-the-art approaches, such as DFL-CNN(Wang, Morariu, and Davis 2018) and NTS(Yang et al. 2018).


2017 ◽  
Vol 31 (9) ◽  
pp. 1749-1769
Author(s):  
Yuebin Wang ◽  
Liqiang Zhang ◽  
Xiaohua Tong ◽  
Suhong Liu ◽  
Tian Fang

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Qingming Leng

Person re-identification, aiming to identify the same pedestrian images across disjoint camera views, is a key technique of intelligent video surveillance. Although existing methods have developed both theories and experimental results, most of effective ones pertain to fully supervised training styles, which suffer the small sample size (SSS) problem a lot, especially in label-insufficient practical applications. To bridge SSS problem and learning model with small labels, a novel semisupervised co-metric learning framework is proposed to learn a discriminative Mahalanobis-like distance matrix for label-insufficient person re-identification. Different from typical co-training task that contains multiview data originally, single-view person images are firstly decomposed into pseudo two views, and then metric learning models are produced and jointly updated based on both pseudo-labels and references iteratively. Experiments carried out on three representative person re-identification datasets show that the proposed method performs better than state of the art and possesses low label sensitivity.


Sign in / Sign up

Export Citation Format

Share Document