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5th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2021

作者： Zhao, Luan Zhang, Xiaofeng Nanchang Hangkong University Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition School of Information Engineering Nanchang China

Constructing the pyramidal architecture for the feature is currently a very effective way to obtain feature information of objects at different scales. Although the feature pyramid can realize the recognition and detection of multi-scale objects in the object detection task well, it still has some limitations. Since the feature information of different levels is often not from the same layer of the network, it is difficult to obtain the feature of different objects information at a certain scale from a certain level feature map of the pyramid network. To solve this problem, we present a novel object detection architecture, named Enhanced Multi-scale Feature Fusion Pyramid Network (EMFFPNet). Our network consists of Enhanced Multi-scale Feature Fusion Module (EMFFM) and Predictor Optimization Module (POM). In EMFFM, Features at different levels can be fused into the Enhanced features as outputs, which are more representative and deterministic. In order to enable the enhanced features to play their respective roles in the pyramid network, we assign different weights to fusion features of different levels in POM. We perform the experiments on the COCO detection benchmark. The experimental results indicate that the performance of our model is much better than the state-of-the-art model. © 2021 IEEE.

关键词： Object detection

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Dense Connected Residual Generative Adversarial Network for Single image Deblurring 5

Dense Connected Residual Generative Adversarial Network for ...

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5th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2021

作者： Qian, Pan Wu, Yeyun Zhang, Xiaofeng Nanchang Hangkong University School of Information Engineering Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang China

Recently, deep convolutional learning has been applied to image deblurring, which greatly improves the performance of single-image blind deblurring algorithms. However, most deep image deblurring models based on convolutional neural networks do not make full use of the information of low-level spatial convolutions, so they achieve relatively poor performance. In this paper, we improved the generator of the Generative Adversarial Network (GAN) and proposed dense networks and residual networks to solve this problem in image deblurring. The network makes full use of the hierarchical features of all convolutional layers. Specifically, we extract the local features of the convolutional layer through the proposed densely connected convolutional layer. Moreover, the dense connections of the residual dense network can connect all layers from the previous state to the current state. Then using local jump connection and feature fusion, it can adaptively learn more effective features from the previous and current local features, making the trained network more extensive. After fully obtaining the dense local features, the global residual connection is used to learn the global feature information in an overall manner. Experimental comparison and verification have proved the effectiveness of our method and achieved excellent results both quantitatively and qualitatively. © 2021 IEEE.

关键词： Generative adversarial networks

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A Survey of Person Re-identification Based on Deep Learning 10

A Survey of Person Re-identification Based on Deep Learning

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10th International Conference on Computing and pattern recognition, ICCPR 2021

作者： Tian, Zimin Chen, Si Wang, Da-Han Lu, Junwen School of Computer and Information Engineering Xiamen University of Technology China Fujian Key Laboratory of Pattern Recognition and Image Understanding China

ISBN: (纸本)9781450390439

Person re-identification (Re-ID) has been a popular research topic in computer vision in recent years, and it has important application value in numerous fields, such as intelligent security. The person Re-ID task is to identify whether the pedestrians appearing under different cameras are the same person. The traditional person Re-ID methods mainly rely on the characteristics of manual design, and it has difficulty in solving the problems of person occlusion, posture change, and illumination variation. With the wide application of deep learning, the person Re-ID based on deep learning has brought new ideas for solving these problems, and has been widely concerned by scholars. This paper summarizes and analyzes the latest research trends of person Re-ID based on deep learning. In our work, the recent research works of person Re-ID are coarsely categorized into the supervised learning methods and the unsupervised learning methods according to whether the pedestrian images in the training set have real labels. We then describe the representative datasets used in the person Re-ID task. Finally, we conclude and discuss the future directions of the person Re-ID based on deep learning. © 2021 ACM.

关键词： Unsupervised learning

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Prototype-guided Unsupervised Domain Adaptation for Semantic Segmentation

Prototype-guided Unsupervised Domain Adaptation for Semantic...

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Control, Electronics and Computer Technology (ICCECT), IEEE International Conference on

作者： Shaoqi Xia Hua Huang Qishen Li Yifan He Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition School of Information Engineering Nanchang Hangkong University Nanchang China School of Information Engineering Nanchang Hangkong University Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition School of Software Nanchang Hangkong University Nanchang China

Semantic segmentation is one of the most important research directions in the field of computer vision, and has a wide range of applications for autonomous driving, medical imaging, intelligent security, etc. Unsupervised domain adaptation is the mainstream research topic in recent years, which can use a large number of labeled source samples to complete the segmentation task in target domain without labeled target samples. In this paper, we propose a prototype-guided unsupervised domain adaptation for semantic segmentation based on ProDA model. Due to lacking of labeled target samples and the prior probability, a prototype distance loss based on target domain is proposed to optimize the distribution of features by measuring the distance between features and the updated prototype and designing an adaptive threshold strategy. Meanwhile, a smoothing loss is proposed to alleviate the impact of source samples on our model and improve the prediction performance of the network. By conducting experiments on the GTA5 to Cityscapes scenarios, the results show that compared with the original model, the loss optimization improves mIoU by1.52.

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Learning with Explicit Shape Priors for Medical image Segmentation

arXiv

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arXiv 2023年

作者： You, Xin He, Junjun Yang, Jie Gu, Yun The Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Shanghai AI Laboratory Shanghai200240 China

Medical image segmentation is a fundamental task for medical image analysis and surgical planning. In recent years, UNet-based networks have prevailed in the field of medical image segmentation. However, convolution-neural networks (CNNs) suffer from limited receptive fields, which fail to model the long-range dependency of organs or tumors. Besides, these models are heavily dependent on the training of the final segmentation head. And existing methods can not well address these two limitations at the same time. Hence, in our work, we proposed a novel shape prior module (SPM), which can explicitly introduce shape priors to promote the segmentation performance of UNet-based models. The explicit shape priors consist of global and local shape priors. The former with coarse shape representations provides networks with capabilities to model global contexts. The latter with finer shape information serves as additional guidance to boost the segmentation performance, which relieves the heavy dependence on the learnable prototype in the segmentation head. To evaluate the effectiveness of SPM, we conduct experiments on three challenging public datasets. And our proposed model achieves state-of-the-art performance. Furthermore, SPM shows an outstanding generalization ability on classic CNNs and recent Transformer-based backbones, which can serve as a plug- and-play structure for the segmentation task of different datasets. Source codes are available at https://***/AlexYouXin/Explicit-Shape-Priors Copyright © 2023, The Authors. All rights reserved.

关键词： image segmentation

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Research on Multi-level Attention-based Human Pose Estimation 2

Research on Multi-level Attention-based Human Pose Estimatio...

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2nd International Conference on Algorithms, High Performance Computing and Artificial Intelligence, AHPCAI 2022

作者： Gao, Jun Huang, Hua Li, Qishen Xia, Shaoqi School of Information Engineering Nanchang Hangkong University Jiangxi Nanchang China School of Software Nanchang Hangkong University Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China

ISBN: (纸本)9798350334784

Detecting human key points from a single image is very challenging due to occlusion, blurring, illumination and scale changes. In this paper, this problem is addressed by designing an effective network structure. Since global and local information plays an important role in reasoning about human body structure and invisible keypoints, Multi-level Attention Network (MAN) is proposed. First, compared with traditional multi-resolution networks, it enables multi-resolution feature maps with greater information variance by generating them directly from the highest resolution feature map, which in turn increases the abundance of feature information after final fusion. Secondly, it effectively integrates global and local information in different resolution feature maps through the Feature Alignment Attention Block(FAAB), and intensifies them in a targeted manner. On the COCO dataset, with HRNet (Sun K. et al [1]) as the baseline network, HRNet of inserted MAN improves 1.1-2.3 AP points over the baseline network. © 2022 IEEE.

关键词： attention mechanism Human Pose Estimation Keypoint detection

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Motion Planning and Tracking Control Method Based on CoppeliaSim for UGV in Irregular Terrain

Motion Planning and Tracking Control Method Based on Coppeli...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Jiale Huang Zhihua Chen Yongbo Zhong Wenrui Zeng Chuanmin Ji key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition and MOE Key Lab of Nondestructive Testing Technology Nanchang Hangkong University Nanchang China

Path planning and tracking algorithms are one of the cores of autonomous navigation technology for unmanned ground vehicle (UGV). In this paper, a local obstacle avoidance method based on event-triggered control is proposed to ensure that the UGV avoids obstacles in the tracking process. First, the path planning and tracking control problem of the UGV is modeled and formulated. Second, the improvement and logical correlation of the relevant algorithms are described in detail. Finally, ajoint simulation of MATLAB and V-rep is performed. The simulation results show that event-triggered control can largely improve the reliability of the UGV path planning and tracking process, and verify the effectiveness of the algorithm.

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Research on attention-based multiscale information fusion with the real-time instance segmentation method 2

Research on attention-based multiscale information fusion wi...

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2nd International Conference on Algorithms, High Performance Computing and Artificial Intelligence, AHPCAI 2022

作者： He, Yifan Huang, Hua Li, Qishen Zhang, Guiwen School of Information Engineering Nanchang Hangkong University Jiangxi Nanchang China School of Software Nanchang Hangkong University Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China

ISBN: (纸本)9798350334784

Instance segmentation is a comprehensive computer vision task that involves a wide range of other tasks. Recently, the study of real-time instance segmentation methods has received more attention for the development of autonomous driving. Although existing real-time instance segmentation methods are fast, their accuracy does not meet practical needs. Most methods go for segmentation based on object detection, and their effectiveness is overly dependent on the effectiveness of detection. This paper proposes a new attention-based multiscale information fusion method based on Cheng, T. et al. [1]. Firstly, the PPM module of the baseline network is replaced with the module Multiscale Context Attention (MSCA) designed in this paper based on the baseline network, which uses atrous convolution with different ratios to obtain information of four scales, and then uses non-local attention to enhance the information of features. It can effectively suppress the interference of redundant information on the instance segmentation results. Secondly, a new feature fusion approach is designed, which no longer uses bilinear interpolation, but sub-pixel up sampling combined with attention. We did experiments related to this module on the coco dataset and demonstrated its effectiveness, with a 0.5% improvement over the baseline network. © 2022 IEEE.

关键词： Computer vision

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Generative Zero-shot image Classification based on Generative Feature Constraint 10

Generative Zero-shot Image Classification based on Generativ...

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10th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022

作者： Yu, Xiao Li, Qishen Huang, Hua Li, Qiufeng School of Information Engineering Nanchang Hangkong University Jiangxi Nanchang China School of Software Nanchang Hangkong University Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang China Jiangxi China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

Zero-shot learning (ZSL) utilizes semantic information that is auxiliary information to transfer knowledge from seen classes to unseen classes, thereby realizing the recognition of unseen classes. The generative methods train the model to generate pseudo image features for the unseen classes, thus transforming ZSL into a supervised learning problem. However, the classification performances of these methods still suffer from weak generalization. To address this issue, the paper proposes a novel Generative Adversarial Network based on Generative Feature Constraint (GFCGAN). First, based on the LsrGAN [1], a semantic constraint module is added to restrict the generated image features by keeping the semantic information consistent. Then, in the case of keeping the semantic relationships between classes consistent, the generated image features of unseen classes are farther away from the feature prototypes of their most similar classes in both semantic and feature space so as to train the generative model and generate high-quality pseudo image features. Finally, the trained classifier is used to classify images of unseen classes. Experiments on standard datasets verify the effectiveness of our model and achieve good results. © 2022 IEEE.

关键词： Zero-shot learning

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NIR-to-VIS Face recognition via Embedding Relations and Coordinates of the Pairwise Features

arXiv

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arXiv 2022年

作者： Cho, MyeongAh Chung, Tae-Young Kim, Taeoh Lee, Sangyoun Image and Video Pattern Recognition Laboratory School of Electrical and Electronic Engineering Yonsei University Korea Republic of

NIR-to-VIS face recognition is identifying faces of two different domains by extracting domain-invariant features. However, this is a challenging problem due to the two different domain characteristics, and the lack of NIR face dataset. In order to reduce domain discrepancy while using the existing face recognition models, we propose a’Relation Module’ which can simply add-on to any face recognition models. The local features extracted from face image contain information of each component of the face. Based on two different domain characteristics, to use the relationships between local features is more domain-invariant than to use it as it is. In addition to these relationships, positional information such as distance from lips to chin or eye to eye, also provides domain-invariant information. In our Relation Module, Relation Layer implicitly captures relationships, and Coordinates Layer models the positional information. Also, our proposed Triplet loss with conditional margin reduces intra-class variation in training, and resulting in additional performance improvements. Different from the general face recognition models, our add-on module does not need to pre-train with the large scale dataset. The proposed module fine-tuned only with CASIA NIR-VIS 2.0 database. With the proposed module, we achieve 14.81% rank-1 accuracy and 15.47% verification rate of 0.1% FAR improvements compare to two baseline models. Copyright © 2022, The Authors. All rights reserved.

关键词： Face recognition

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