A Global-Local Blur Disentangling Network for Dynamic Scene Deblurring

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.

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Person Reidentification Model Based on Multiattention Modules and Multiscale Residuals

Complexity ◽

10.1155/2021/6673461 ◽

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.

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Parts Semantic Segmentation Aware Representation Learning for Person Re-Identification

Applied Sciences ◽

10.3390/app9061239 ◽

2019 ◽

Vol 9 (6) ◽

pp. 1239 ◽

Cited By ~ 1

Author(s):

Hua Gao ◽

Shengyong Chen ◽

Zhaosheng Zhang

Keyword(s):

Semantic Segmentation ◽

Representation Learning ◽

Local Features ◽

Point Of View ◽

Body Parts ◽

Feature Maps ◽

Benchmark Datasets ◽

Background Clutter ◽

Three Body ◽

Global And Local

Person re-identification is a typical computer vision problem which aims at matching pedestrians across disjoint camera views. It is challenging due to the misalignment of body parts caused by pose variations, background clutter, detection errors, camera point of view variation, different accessories and occlusion. In this paper, we propose a person re-identification network which fuses global and local features, to deal with part misalignment problem. The network is a four-branch convolutional neural network (CNN) which learns global person appearance and local features of three human body parts respectively. Local patches, including the head, torso and lower body, are segmented by using a U_Net semantic segmentation CNN architecture. All four feature maps are then concatenated and fused to represent a person image. We propose a DropParts method to solve the parts missing problem, with which the local features are weighed according to the number of parts found by semantic segmentation. Since three body parts are well aligned, the approach significantly improves person re-identification. Experiments on the standard benchmark datasets, such as Market1501, CUHK03 and DukeMTMC-reID datasets, show the effectiveness of our proposed pipeline.

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Hyperspectral Images Classification Based on Dense Convolutional Networks with Spectral-Wise Attention Mechanism

Remote Sensing ◽

10.3390/rs11020159 ◽

2019 ◽

Vol 11 (2) ◽

pp. 159 ◽

Cited By ~ 33

Author(s):

Bei Fang ◽

Ying Li ◽

Haokui Zhang ◽

Jonathan Chan

Keyword(s):

Classification Performance ◽

Attention Mechanism ◽

Experimental Results ◽

Hyperspectral Images ◽

Spectral Features ◽

Competitive Performance ◽

Feature Maps ◽

Convolutional Network ◽

Convolutional Networks ◽

Spatial Features

Hyperspectral images (HSIs) data that is typically presented in 3-D format offers an opportunity for 3-D networks to extract spectral and spatial features simultaneously. In this paper, we propose a novel end-to-end 3-D dense convolutional network with spectral-wise attention mechanism (MSDN-SA) for HSI classification. The proposed MSDN-SA exploits 3-D dilated convolutions to simultaneously capture the spectral and spatial features at different scales, and densely connects all 3-D feature maps with each other. In addition, a spectral-wise attention mechanism is introduced to enhance the distinguishability of spectral features, which improves the classification performance of the trained models. Experimental results on three HSI datasets demonstrate that our MSDN-SA achieves competitive performance for HSI classification.

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Dual Branch Attention Network for Person Re-Identification

Sensors ◽

10.3390/s21175839 ◽

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%.

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

Symmetry ◽

10.3390/sym13101838 ◽

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.

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U2-ONet: A Two-Level Nested Octave U-Structure Network with a Multi-Scale Attention Mechanism for Moving Object Segmentation

Remote Sensing ◽

10.3390/rs13010060 ◽

2020 ◽

Vol 13 (1) ◽

pp. 60

Author(s):

Chenjie Wang ◽

Chengyuan Li ◽

Jun Liu ◽

Bin Luo ◽

Xin Su ◽

...

Keyword(s):

Moving Objects ◽

Object Segmentation ◽

Contextual Information ◽

Attention Mechanism ◽

Moving Object ◽

Feature Maps ◽

Moving Object Segmentation ◽

Practical Applications ◽

Multi Scale ◽

Spatial Redundancy

Most scenes in practical applications are dynamic scenes containing moving objects, so accurately segmenting moving objects is crucial for many computer vision applications. In order to efficiently segment all the moving objects in the scene, regardless of whether the object has a predefined semantic label, we propose a two-level nested octave U-structure network with a multi-scale attention mechanism, called U2-ONet. U2-ONet takes two RGB frames, the optical flow between these frames, and the instance segmentation of the frames as inputs. Each stage of U2-ONet is filled with the newly designed octave residual U-block (ORSU block) to enhance the ability to obtain more contextual information at different scales while reducing the spatial redundancy of the feature maps. In order to efficiently train the multi-scale deep network, we introduce a hierarchical training supervision strategy that calculates the loss at each level while adding knowledge-matching loss to keep the optimization consistent. The experimental results show that the proposed U2-ONet method can achieve a state-of-the-art performance in several general moving object segmentation datasets.

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