scholarly journals Mis-Classified Vector Guided Softmax Loss for Face Recognition

2020 ◽  
Vol 34 (07) ◽  
pp. 12241-12248 ◽  
Author(s):  
Xiaobo Wang ◽  
Shifeng Zhang ◽  
Shuo Wang ◽  
Tianyu Fu ◽  
Hailin Shi ◽  
...  
Keyword(s):  
Face Recognition ◽  
Loss Function ◽  
State Of The Art ◽  
Feature Learning ◽  
Ground Truth ◽  
Significant Progress ◽  
Deep Convolutional Neural Networks ◽  
Face Features ◽  
Discriminative Feature ◽  
Feature Mining

Face recognition has witnessed significant progress due to the advances of deep convolutional neural networks (CNNs), the central task of which is how to improve the feature discrimination. To this end, several margin-based (e.g., angular, additive and additive angular margins) softmax loss functions have been proposed to increase the feature margin between different classes. However, despite great achievements have been made, they mainly suffer from three issues: 1) Obviously, they ignore the importance of informative features mining for discriminative learning; 2) They encourage the feature margin only from the ground truth class, without realizing the discriminability from other non-ground truth classes; 3) The feature margin between different classes is set to be same and fixed, which may not adapt the situations very well. To cope with these issues, this paper develops a novel loss function, which adaptively emphasizes the mis-classified feature vectors to guide the discriminative feature learning. Thus we can address all the above issues and achieve more discriminative face features. To the best of our knowledge, this is the first attempt to inherit the advantages of feature margin and feature mining into a unified loss function. Experimental results on several benchmarks have demonstrated the effectiveness of our method over state-of-the-art alternatives. Our code is available at http://www.cbsr.ia.ac.cn/users/xiaobowang/.

scholarly journals Double Additive Margin Softmax Loss for Face Recognition

2019 ◽  
Vol 10 (1) ◽  
pp. 60 ◽  
Author(s):  
Shengwei Zhou ◽  
Caikou Chen ◽  
Guojiang Han ◽  
Xielian Hou
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Loss Function ◽  
State Of The Art ◽  
Feature Learning ◽  
Loss Functions ◽  
Deep Convolutional Neural Networks ◽  
Large Margin ◽  
Face Features ◽  
Geometrical Explanation

Learning large-margin face features whose intra-class variance is small and inter-class diversity is one of important challenges in feature learning applying Deep Convolutional Neural Networks (DCNNs) for face recognition. Recently, an appealing line of research is to incorporate an angular margin in the original softmax loss functions for obtaining discriminative deep features during the training of DCNNs. In this paper we propose a novel loss function, termed as double additive margin Softmax loss (DAM-Softmax). The presented loss has a clearer geometrical explanation and can obtain highly discriminative features for face recognition. Extensive experimental evaluation of several recent state-of-the-art softmax loss functions are conducted on the relevant face recognition benchmarks, CASIA-Webface, LFW, CALFW, CPLFW, and CFP-FP. We show that the proposed loss function consistently outperforms the state-of-the-art.

scholarly journals Centralized Ranking Loss with Weakly Supervised Localization for Fine-Grained Object Retrieval

2018 ◽  
Author(s):  
Xiawu Zheng ◽  
Rongrong Ji ◽  
Xiaoshuai Sun ◽  
Yongjian Wu ◽  
Feiyue Huang ◽  
...  
Keyword(s):  
State Of The Art ◽  
Feature Learning ◽  
Target Object ◽  
Object Retrieval ◽  
Unified Framework ◽  
Fine Grained ◽  
Discriminative Feature ◽  
Triplet Loss ◽  
Weakly Supervised ◽  
Ranking Loss

Fine-grained object retrieval has attracted extensive research focus recently. Its state-of-the-art schemesare typically based upon convolutional neural network (CNN) features. Despite the extensive progress, two issues remain open. On one hand, the deep features are coarsely extracted at image level rather than precisely at object level, which are interrupted by background clutters. On the other hand, training CNN features with a standard triplet loss is time consuming and incapable to learn discriminative features. In this paper, we present a novel fine-grained object retrieval scheme that conquers these issues in a unified framework. Firstly, we introduce a novel centralized ranking loss (CRL), which achieves a very efficient (1,000times training speedup comparing to the triplet loss) and discriminative feature learning by a ?centralized? global pooling. Secondly, a weakly supervised attractive feature extraction is proposed, which segments object contours with top-down saliency. Consequently, the contours are integrated into the CNN response map to precisely extract features ?within? the target object. Interestingly, we have discovered that the combination of CRL and weakly supervised learning can reinforce each other. We evaluate the performance ofthe proposed scheme on widely-used benchmarks including CUB200-2011 and CARS196. We havereported significant gains over the state-of-the-art schemes, e.g., 5.4% over SCDA [Wei et al., 2017]on CARS196, and 3.7% on CUB200-2011.  

scholarly journals Illumination-robust face recognition based on deep convolutional neural networks architectures

2020 ◽  
Vol 18 (2) ◽  
pp. 1015
Author(s):  
Ridha Ilyas Bendjillali ◽  
Mohammed Beladgham ◽  
Khaled Merit ◽  
Abdelmalik Taleb-Ahmed
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Convolutional Neural Networks ◽  
Feature Learning ◽  
Histogram Equalization ◽  
Detection Algorithm ◽  
Deep Convolutional Neural Networks ◽  
Biometric Technology ◽  
Equalization Algorithm ◽  
Robust Face Recognition

<p><span>In the last decade, facial recognition techniques are considered the most important fields of research in biometric technology. In this research paper, we present a Face Recognition (FR) system divided into three steps: The Viola-Jones face detection algorithm, facial image enhancement using Modified Contrast Limited Adaptive Histogram Equalization algorithm (M-CLAHE), and feature learning for classification. For learning the features followed by classification we used VGG16, ResNet50 and Inception-v3 Convolutional Neural Networks (CNN) architectures for the proposed system. Our experimental work was performed on the Extended Yale B database and CMU PIE face database. Finally, the comparison with the other methods on both databases shows the robustness and effectiveness of the proposed approach. Where the Inception-v3 architecture has achieved a rate of 99, 44% and 99, 89% respectively.</span></p>

scholarly journals Weakly Supervised Learning of Single-Cell Feature Embeddings

2018 ◽  
Author(s):  
Juan C. Caicedo ◽  
Claire McQuin ◽  
Allen Goodman ◽  
Shantanu Singh ◽  
Anne E. Carpenter
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Feature Learning ◽  
Experimental Treatment ◽  
Ground Truth ◽  
Learning Approaches ◽  
Deep Convolutional Neural Networks ◽  
Experimental Conditions ◽  
Weakly Supervised ◽  
Experimental Treatments

AbstractWe study the problem of learning representations for single cells in microscopy images to discover biological relationships between their experimental conditions. Many new applications in drug discovery and functional genomics require capturing the morphology of individual cells as comprehensively as possible. Deep convolutional neural networks (CNNs) can learn powerful visual representations, but require ground truth for training; this is rarely available in biomedical profiling experiments. While we do not know which experimental treatments produce cells that look alike, we do know that cells exposed to the same experimental treatment should generally look similar. Thus, we explore training CNNs using a weakly supervised approach that uses this information for feature learning. In addition, the training stage is regularized to control for unwanted variations using mixup or RNNs. We conduct experiments on two different datasets; the proposed approach yields single-cell embeddings that are more accurate than the widely adopted classical features, and are competitive with previously proposed transfer learning approaches.

scholarly journals Multiscale Meets Spatial Awareness: An Efficient Attention Guidance Network for Human Parsing

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Fan Zhou ◽  
Enbo Huang ◽  
Zhuo Su ◽  
Ruomei Wang
Keyword(s):  
State Of The Art ◽  
Spatial Awareness ◽  
Significant Progress ◽  
Deep Convolutional Neural Networks ◽  
Edge Information ◽  
Comprehensive Evaluations ◽  
Human Pose ◽  
Human Image ◽  
Public Datasets ◽  
Spatial Semantics

Human parsing, which aims at resolving human body and clothes into semantic part regions from an human image, is a fundamental task in human-centric analysis. Recently, the approaches for human parsing based on deep convolutional neural networks (DCNNs) have made significant progress. However, hierarchically exploiting multiscale and spatial contexts as convolutional features is still a hurdle to overcome. In order to boost the scale and spatial awareness of a DCNN, we propose two effective structures, named “Attention SPP and Attention RefineNet,” to form a Mutual Attention operation, to exploit multiscale and spatial semantics different from the existing approaches. Moreover, we propose a novel Attention Guidance Network (AG-Net), a simple yet effective architecture without using bells and whistles (such as human pose and edge information), to address human parsing tasks. Comprehensive evaluations on two public datasets well demonstrate that the AG-Net outperforms the state-of-the-art networks.

scholarly journals Cross-Modality 2D-3D Face Recognition via Multiview Smooth Discriminant Analysis Based on ELM

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yi Jin ◽  
Jiuwen Cao ◽  
Qiuqi Ruan ◽  
Xueqiao Wang
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Feature Learning ◽  
Feature Space ◽  
Feedforward Neural Networks ◽  
3D Face Recognition ◽  
3D Face ◽  
Face Features ◽  
Fast Training ◽  
Learning Machine

In recent years, 3D face recognition has attracted increasing attention from worldwide researchers. Rather than homogeneous face data, more and more applications require flexible input face data nowadays. In this paper, we propose a new approach for cross-modality 2D-3D face recognition (FR), which is called Multiview Smooth Discriminant Analysis (MSDA) based on Extreme Learning Machines (ELM). Adding the Laplacian penalty constrain for the multiview feature learning, the proposed MSDA is first proposed to extract the cross-modality 2D-3D face features. The MSDA aims at finding a multiview learning based common discriminative feature space and it can then fully utilize the underlying relationship of features from different views. To speed up the learning phase of the classifier, the recent popular algorithm named Extreme Learning Machine (ELM) is adopted to train the single hidden layer feedforward neural networks (SLFNs). To evaluate the effectiveness of our proposed FR framework, experimental results on a benchmark face recognition dataset are presented. Simulations show that our new proposed method generally outperforms several recent approaches with a fast training speed.

scholarly journals Margin CosReid Network for Pedestrian Re-Identification

2021 ◽  
Vol 11 (4) ◽  
pp. 1775
Author(s):  
Xiao Yun ◽  
Min Ge ◽  
Yanjing Sun ◽  
Kaiwen Dong ◽  
Xiaofeng Hou
Keyword(s):  
Feature Extraction ◽  
Loss Function ◽  
State Of The Art ◽  
Learning Model ◽  
Identification Methods ◽  
Discriminative Feature ◽  
Convergence Problems

This paper proposes a margin CosReid network for effective pedestrian re-identification. Aiming to overcome the overfitting, gradient explosion, and loss function non-convergence problems caused by traditional CNNs, the proposed GBNeck model can realize a faster, stronger generalization, and more discriminative feature extraction task. Furthermore, to enhance the classification ability of the softmax loss function within classes, the margin cosine softmax loss (MCSL) is proposed through a boundary margin introduction to ensure intraclass compactness and interclass separability of the learning depth features and thus to build a stronger metric-based learning model for pedestrian re-identification. The effectiveness of the margin CosReid network was verified on the mainstream datasets Market-1501 and DukeMTMC-reID compared with other state-of-the-art pedestrian re-identification methods.

scholarly journals A New Dataset and Deep Residual Spectral Spatial Network for Hyperspectral Image Classification

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 561 ◽  
Author(s):  
Yiming Xue ◽  
Dan Zeng ◽  
Fansheng Chen ◽  
Yueming Wang ◽  
Zhijiang Zhang
Keyword(s):  
Neural Networks ◽  
Loss Function ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Feature Learning ◽  
Spatial Network ◽  
Hyperspectral Image Classification ◽  
Classification Framework ◽  
Prediction Confidence ◽  
Art Methods

Due to the limited varieties and sizes of existing public hyperspectral image (HSI) datasets, the classification accuracies are higher than 99% with convolutional neural networks (CNNs). In this paper, we presented a new HSI dataset named Shandong Feicheng, whose size and pixel quantity are much larger. It also has a larger intra-class variance and a smaller inter-class variance. State-of-the-art methods were compared on it to verify its diversity. Otherwise, to reduce overfitting caused by the imbalance between high dimension and small quantity of labeled HSI data, existing CNNs for HSI classification are relatively shallow and suffer from low capacity of feature learning. To solve this problem, we proposed an HSI classification framework named deep residual spectral spatial setwork (DRSSN). By using shortcut connection structure, which is an asymmetry structure, DRSSN can be deeper to extract features with better discrimination. In addition, to alleviate insufficient training caused by unbalanced sample sizes between easily and hard classified samples, we proposed a novel training loss function named sample balanced loss, which allocated weights to the losses of samples according to their prediction confidence. Experimental results on two popular datasets and our proposed dataset showed that our proposed network could provide competitive results compared with state-of-the-art methods.

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