scholarly journals Dual Branch Attention Network for Person Re-Identification

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5839
Author(s):  
Denghua Fan ◽  
Liejun Wang ◽  
Shuli Cheng ◽  
Yongming Li
Keyword(s):  
Local Features ◽  
Attention Mechanism ◽  
Camera Tracking ◽  
Global Features ◽  
Attention Network ◽  
Fine Grained ◽  
Rank List ◽  
Security Problem ◽  
Spatial Dimensions ◽  
Global And Local

As a sub-direction of image retrieval, person re-identification (Re-ID) is usually used to solve the security problem of cross camera tracking and monitoring. A growing number of shopping centers have recently attempted to apply Re-ID technology. One of the development trends of related algorithms is using an attention mechanism to capture global and local features. We notice that these algorithms have apparent limitations. They only focus on the most salient features without considering certain detailed features. People’s clothes, bags and even shoes are of great help to distinguish pedestrians. We notice that global features usually cover these important local features. Therefore, we propose a dual branch network based on a multi-scale attention mechanism. This network can capture apparent global features and inconspicuous local features of pedestrian images. Specifically, we design a dual branch attention network (DBA-Net) for better performance. These two branches can optimize the extracted features of different depths at the same time. We also design an effective block (called channel, position and spatial-wise attention (CPSA)), which can capture key fine-grained information, such as bags and shoes. Furthermore, based on ID loss, we use complementary triplet loss and adaptive weighted rank list loss (WRLL) on each branch during the training process. DBA-Net can not only learn semantic context information of the channel, position, and spatial dimensions but can integrate detailed semantic information by learning the dependency relationships between features. Extensive experiments on three widely used open-source datasets proved that DBA-Net clearly yielded overall state-of-the-art performance. Particularly on the CUHK03 dataset, the mean average precision (mAP) of DBA-Net achieved 83.2%.

scholarly journals Person Reidentification Model Based on Multiattention Modules and Multiscale Residuals

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yongyi Li ◽  
Shiqi Wang ◽  
Shuang Dong ◽  
Xueling Lv ◽  
Changzhi Lv ◽  
...  
Keyword(s):  
Local Features ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Original Network ◽  
Fine Grained ◽  
Backbone Network ◽  
Model Based ◽  
Local Branch ◽  
Feature Expression ◽  
Global And Local

At present, person reidentification based on attention mechanism has attracted many scholars’ interests. Although attention module can improve the representation ability and reidentification accuracy of Re-ID model to a certain extent, it depends on the coupling of attention module and original network. In this paper, a person reidentification model that combines multiple attentions and multiscale residuals is proposed. The model introduces combined attention fusion module and multiscale residual fusion module in the backbone network ResNet 50 to enhance the feature flow between residual blocks and better fuse multiscale features. Furthermore, a global branch and a local branch are designed and applied to enhance the channel aggregation and position perception ability of the network by utilizing the dual ensemble attention module, as along as the fine-grained feature expression is obtained by using multiproportion block and reorganization. Thus, the global and local features are enhanced. The experimental results on Market-1501 dataset and DukeMTMC-reID dataset show that the indexes of the presented model, especially Rank-1 accuracy, reach 96.20% and 89.59%, respectively, which can be considered as a progress in Re-ID.

scholarly journals Object–Part Registration–Fusion Net for Fine-Grained Image Classification

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1838
Author(s):  
Chih-Wei Lin ◽  
Mengxiang Lin ◽  
Jinfu Liu
Keyword(s):  
Feature Fusion ◽  
Bird Species ◽  
Image Understanding ◽  
Local Features ◽  
Feature Maps ◽  
Global Features ◽  
Fine Grained ◽  
Object Part ◽  
Global And Local ◽  
Fusion Feature

Classifying fine-grained categories (e.g., bird species, car, and aircraft types) is a crucial problem in image understanding and is difficult due to intra-class and inter-class variance. Most of the existing fine-grained approaches individually utilize various parts and local information of objects to improve the classification accuracy but neglect the mechanism of the feature fusion between the object (global) and object’s parts (local) to reinforce fine-grained features. In this paper, we present a novel framework, namely object–part registration–fusion Net (OR-Net), which considers the mechanism of registration and fusion between an object (global) and its parts’ (local) features for fine-grained classification. Our model learns the fine-grained features from the object of global and local regions and fuses these features with the registration mechanism to reinforce each region’s characteristics in the feature maps. Precisely, OR-Net consists of: (1) a multi-stream feature extraction net, which generates features with global and various local regions of objects; (2) a registration–fusion feature module calculates the dimension and location relationships between global (object) regions and local (parts) regions to generate the registration information and fuses the local features into the global features with registration information to generate the fine-grained feature. Experiments execute symmetric GPU devices with symmetric mini-batch to verify that OR-Net surpasses the state-of-the-art approaches on CUB-200-2011 (Birds), Stanford-Cars, and Stanford-Aircraft datasets.

scholarly journals A Global-Local Blur Disentangling Network for Dynamic Scene Deblurring

2021 ◽  
Vol 11 (5) ◽  
pp. 2174
Author(s):  
Xiaoguang Li ◽  
Feifan Yang ◽  
Jianglu Huang ◽  
Li Zhuo
Keyword(s):  
Local Features ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Dynamic Scene ◽  
Feature Maps ◽  
Training Scheme ◽  
Real Scene ◽  
Global And Local

Images captured in a real scene usually suffer from complex non-uniform degradation, which includes both global and local blurs. It is difficult to handle the complex blur variances by a unified processing model. We propose a global-local blur disentangling network, which can effectively extract global and local blur features via two branches. A phased training scheme is designed to disentangle the global and local blur features, that is the branches are trained with task-specific datasets, respectively. A branch attention mechanism is introduced to dynamically fuse global and local features. Complex blurry images are used to train the attention module and the reconstruction module. The visualized feature maps of different branches indicated that our dual-branch network can decouple the global and local blur features efficiently. Experimental results show that the proposed dual-branch blur disentangling network can improve both the subjective and objective deblurring effects for real captured images.

scholarly journals Hybrid Attention Network for Language-Based Person Search

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5279
Author(s):  
Yang Li ◽  
Huahu Xu ◽  
Junsheng Xiao
Keyword(s):  
Image Features ◽  
Attention Mechanism ◽  
Feature Representation ◽  
Semantic Features ◽  
Retrieval Task ◽  
Attention Network ◽  
Fine Grained ◽  
Person Search ◽  
High Level ◽  
Language Description

Language-based person search retrieves images of a target person using natural language description and is a challenging fine-grained cross-modal retrieval task. A novel hybrid attention network is proposed for the task. The network includes the following three aspects: First, a cubic attention mechanism for person image, which combines cross-layer spatial attention and channel attention. It can fully excavate both important midlevel details and key high-level semantics to obtain better discriminative fine-grained feature representation of a person image. Second, a text attention network for language description, which is based on bidirectional LSTM (BiLSTM) and self-attention mechanism. It can better learn the bidirectional semantic dependency and capture the key words of sentences, so as to extract the context information and key semantic features of the language description more effectively and accurately. Third, a cross-modal attention mechanism and a joint loss function for cross-modal learning, which can pay more attention to the relevant parts between text and image features. It can better exploit both the cross-modal and intra-modal correlation and can better solve the problem of cross-modal heterogeneity. Extensive experiments have been conducted on the CUHK-PEDES dataset. Our approach obtains higher performance than state-of-the-art approaches, demonstrating the advantage of the approach we propose.

scholarly journals SAN-GAL: Spatial Attention Network Guided by Attribute Label for Person Re-identification

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Shaoqi Hou ◽  
Chunhui Liu ◽  
Kangning Yin ◽  
Yiyin Ding ◽  
Zhiguo Wang ◽  
...  
Keyword(s):  
Spatial Attention ◽  
Binary Classification ◽  
Test Results ◽  
Global Features ◽  
Matching Problem ◽  
Heat Map ◽  
Attention Network ◽  
Fine Grained ◽  
Attribute Classification ◽  
High Degree

Person Re-identification (Re-ID) is aimed at solving the matching problem of the same pedestrian at a different time and in different places. Due to the cross-device condition, the appearance of different pedestrians may have a high degree of similarity; at this time, using the global features of pedestrians to match often cannot achieve good results. In order to solve these problems, we designed a Spatial Attention Network Guided by Attribute Label (SAN-GAL), which is a dual-trace network containing both attribute classification and Re-ID. Different from the previous approach of simply adding a branch of attribute binary classification network, our SAN-GAL is mainly divided into two connecting steps. First, with attribute labels as guidance, we generate Attribute Attention Heat map (AAH) through Grad-CAM algorithm to accurately locate fine-grained attribute areas of pedestrians. Then, the Attribute Spatial Attention Module (ASAM) is constructed according to the AHH which is taken as the prior knowledge and introduced into the Re-ID network to assist in the discrimination of the Re-ID task. In particular, our SAN-GAL network can integrate the local attribute information and global ID information of pedestrians without introducing additional attribute region annotation, which has good flexibility and adaptability. The test results on Market1501 and DukeMTMC-reID show that our SAN-GAL can achieve good results and can achieve 85.8% Rank-1 accuracy on DukeMTMC-reID dataset, which is obviously competitive compared with most Re-ID algorithms.

INFLUENCE OF mRNA FEATURES ON siRNA INTERFERENCE EFFICACY

2013 ◽  
Vol 11 (03) ◽  
pp. 1341004 ◽  
Author(s):  
YUANNING LIU ◽  
YAPING CHANG ◽  
CHAO ZHANG ◽  
QINGKAI WEI ◽  
JINGBO CHEN ◽  
...  
Keyword(s):  
Binding Site ◽  
Gc Content ◽  
Prediction Method ◽  
Secondary Structures ◽  
Local Features ◽  
Global Features ◽  
Small Interference Rna ◽  
Rnai Technology ◽  
Sirna Design ◽  
Global And Local

Design of small interference RNA (siRNA) is one of the most important steps in effectively applying the RNA interference (RNAi) technology. The current siRNA design often produces inconsistent design results, which often fail to reliably select siRNA with clear silencing effects. We propose that when designing siRNA, one should consider mRNA global features and near siRNA-binding site local features. By a linear regression study, we discovered strong correlations between inhibitory efficacy and both mRNA global features and neighboring local features. This paper shows that, on average, less GC content, fewer stem secondary structures, and more loop secondary structures of mRNA at both global and local flanking regions of the siRNA binding sites lead to stronger inhibitory efficacy. Thus, the use of mRNA global features and near siRNA-binding site local features are essential to successful gene silencing and hence, a better siRNA design. We use a random forest model to predict siRNA efficacy using siRNA features, mRNA features, and near siRNA binding site features. Our prediction method achieved a correlation coefficient of 0.7 in 10-fold cross validation in contrast to 0.63 when using siRNA features only. Our study demonstrates that considering mRNA and near siRNA binding site features helps improve siRNA design accuracy. The findings may also be helpful in understanding binding efficacy between microRNA and mRNA.

Facial Expression Recognition Based on Features Fusion

2010 ◽  
Vol 20-23 ◽  
pp. 1253-1259
Author(s):  
Chang Jun Zhou ◽  
Xiao Peng Wei ◽  
Qiang Zhang
Keyword(s):  
Principal Component ◽  
Local Features ◽  
Support Vector ◽  
Expression Recognition ◽  
Global Features ◽  
Features Fusion ◽  
Face Expression ◽  
The Face ◽  
Value Decomposition ◽  
Global And Local

In this paper, we propose a novel algorithm for facial recognition based on features fusion in support vector machine (SVM). First, some local features and global features from pre-processed face images are obtained. The global features are obtained by making use of singular value decomposition (SVD). At the same time, the local features are obtained by utilizing principal component analysis (PCA) to extract the principal Gabor features. Finally, the feature vectors which are fused with global and local features are used to train SVM to realize the face expression recognition, and the computer simulation illustrates the effectivity of this method on the JAFFE database.

Canonical Correlation Analysis on Fusion of Global and Local Features of Face Recognition

2014 ◽  
Vol 926-930 ◽  
pp. 3598-3603
Author(s):  
Xiao Xiong ◽  
Guo Fa Hao ◽  
Peng Zhong
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Local Features ◽  
Global Features ◽  
Features Fusion ◽  
Local Characteristics ◽  
Face Recognition System ◽  
Global And Local ◽  
Effective Integration

Face recognition belongs to the important content of the biometric identification, which is a important method in research of image processing and pattern recognition. It can effectively overcome the traditional authentication defects Through the facial recognition technology. At present, face recognition under ideal state research made some achievements, but the changes in light, shade, expression, posture changes the interference factors such as face recognition is still exist many problems. For this, put forward the integration of global and local features of face recognition research. Practice has proved that through the effective integration of global features and local characteristics, build based on global features and local features fusion face recognition system, can improve the recognition rate of face recognition, face recognition application benefit.

scholarly journals The Influence of Inhibition of Return and Level-Priming on the Global Precedence Effect

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Yan Wu ◽  
Qi Li ◽  
Yuanzi Liu
Keyword(s):  
Inhibition Of Return ◽  
Local Features ◽  
Precedence Effect ◽  
Global Precedence ◽  
Global Features ◽  
Hierarchical Processing ◽  
Inhibition Effect ◽  
Hierarchical Stimuli ◽  
Priming Paradigm ◽  
Global And Local

The aim of the reported experiment was to investigate the effects of inhibition of return (IOR) and level-priming on the global precedence effect (GPE). The classical hierarchical stimuli combined with IOR and the level-priming paradigm were used. The participants selectively attended to the global or local features of compound numerals. The results showed that IOR inhibited the response to the global and local features; moreover, the inhibition effect on the perception of the global features was stronger than that of the local features in the stage of inhibitory processing, resulting in the disappearance of GPE. However, level-priming promoted the response to global and local features, and the promotion effect was stronger on local features, leading to the disappearance of GPE as well. These findings suggested that hierarchical processing was affected by IOR and level-priming, which were correlated with selective attention. Thus, it indicated that global precedence could be involved in attentional mechanisms.

scholarly journals High-Resolution Remote Sensing Image Integrity Authentication Method Considering Both Global and Local Features

2020 ◽  
Vol 9 (4) ◽  
pp. 254
Author(s):  
Xingang Zhang ◽  
Haowen Yan ◽  
Liming Zhang ◽  
Hao Wang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Local Features ◽  
Tamper Localization ◽  
Global Features ◽  
Key Points ◽  
Recognition Ability ◽  
Decision Making Processes ◽  
Image Integrity ◽  
Global And Local

Content integrity of high-resolution remote sensing (HRRS) images is the premise of its usability. Existing HRRS image integrity authentication methods are mostly binary decision-making processes, which cannot provide a further interpretable information (e.g., tamper localization, tamper type determination). Due to this reason, a robust HRRS images integrity authentication algorithm using perceptual hashing technology considering both global and local features is proposed in this paper. It extracts global features by the efficient recognition ability of Zernike moments to texture information. Meanwhile, Features from Accelerated Segment Test (FAST) key points are applied to local features construction and tamper localization. By applying the concept of multi-feature combination to the integrity authentication of HRRS images, the authentication process is more convincing in comparison to existing algorithms. Furthermore, an interpretable authentication result can be given. The experimental results show that the algorithm proposed in this paper is highly robust to the content retention operation, has a strong sensitivity to the content changing operations, and the result of tampering localization is more precise comparing with existing algorithms.

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