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International Conference on Awareness Science and Technology (iCAST)

作者： Fengzhi Wang Qinzhi Lv Lijuan Liu College of Computer and Information Engineering Xiamen University of Technology Xiamen China Fujian Key Laboratory of Pattern Recognition and Image Understanding

Passenger flow prediction is vitally significant for intelligent transportation systems (ITS). Most of the studies typically focus on the passenger flow prediction for an individual station, and only capture the temporal features without considering any spatial features. Constructing a passenger flow prediction model for multiple stations, or even a whole network, is more valuable for practical applications. Therefore, we develop a dynamic spatio-temporal network (DSTNet) with a self-attention (SA) mechanism for multi-station passenger flow prediction. A dynamic graph convolutional network (DGCN) is applied for the spatial feature extraction, and gated recurrent unit (GRU) is combined to learn the temporal features. SA is applied to further assign the weights for the extracted spatio-temporal features. The Experiment has been conducted on the passenger flow in the Xiamen bus rapid transit (BRT). The results demonstrate that the proposed DSTNet with SA (SA-DSTNet) outperforms the baselines in the multi-station passenger flow prediction task.

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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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NLFA: A Non Local Fusion Alignment Module for Multi-Scale Feature in Object Detection 3rd

NLFA: A Non Local Fusion Alignment Module for Multi-Scale Fe...

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3rd International Symposium on Automation, Mechanical and Design Engineering, SAMDE 2022

作者： Xue, Honghui Ma, Jinshan Cai, Zheyi Fu, Junfang Guo, Feng Weng, Wei Dong, Yunxin Zhang, Zhenchang College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou China Fujian Zhongke Zhongxin Intelligent Technology Co. Ltd Fuzhou China Fujian Newland Auto-ID Tech. Co. Ltd Fuzhou China Department of Computer and Information Engineering Xiamen University of Technology Xiamen China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen China

ISBN: (纸本)9783031400698

Recently, in order to pursue better detection results, more convolutional layers and deeper networks are a direction pursued by everyone. However, more and more down-sampling convolution or up-sampling operations generate feature maps of different scales, which makes it difficult to avoid the loss of detailed information of the image, and the distribution of different scales features will be misaligned. In particular, the loss and dislocation of the target boundary information will affect the features learned by the model and reduce the accuracy. This paper proposes a feature alignment method based on non-local idea, and designed two modules—Non Local Align Module (NLA) and Channel Fusion Augment Module (CFA). At the same time, the neighborhood calculation algorithm is also designed for it, which strengthens the binding force on the calculation of boundary information. These two modules can be easily embedded into the current mainstream object detection network to improve the detection effect of the model. Compared to the previous model, the network using our NLA module and CFA module achieves better results than the original model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Interactive Semantic Segmentation With Weak Supervision 22

Interactive Semantic Segmentation With Weak Supervision

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8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Gong, Lei Wang, Da-Han Wu, Yun Ye, Hai-Li Zhu, Chen-Yan School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China Medical Diagnostic Systems Co. Ltd. Xiamen361000 China

ISBN: (纸本)9781450396110

At present, the most advanced semantic segmentation model training mainly relies on pixel-level annotation, that is, annotating the category of each pixel of an image. Such annotation usually is time-consuming and expensive, especially for special applications that require expert annotation. The weakly-supervised segmentation method using the point-level supervision information has been investigated which however has great problems that the supervision information is quite limited and the performance is far from fully supervised methods. In this paper, we proposes an novelty interactive image segmentation method based on weak supervision, which allows multiple feedbacks of easily obtained weakly supervised information and improves the efficiency of utility of the supervision information. In the downstream task (interactive image segmentation), supervised information at the point level is used for many times, which makes the connection between pixels in the upstream task become closer and improves the segmentation accuracy. First, image-level tags are used to train the classification network. Then the pseudo-semantic labels are generated and put into the interactive segmentation network for training, and an almost completely supervised CNN is obtained, which further improves the performance and provides operability for human-computer interaction. The proposed method achieves promising semantic segmentation results that are close to those obtained by strongly supervised segmentation methods on the PASCAL VOC 2012 datasets. © 2022 ACM.

关键词： Semantic Segmentation

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Journal of Network Intelligence

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Journal of Network Intelligence 2022年第4期7卷 848-861页

作者： Weng, Wei Li, Tong Liao, Jian-Chao Guo, Feng Chen, Fen Wei, Bo-Wen College of Computer and Information Engineering Xiamen University of Technology Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Newland Auto-ID Tech. Co. Ltd Fuzhou350015 China Xiamen Fuyun Information Tech. Co. Ltd Xiamen361008 China

Graph clustering is a fundamental method for studying complex networks. Some existing approaches focus on the graph data without attributed information. However, graph data in the real world generally have attribute information, which can better describe complex networks. Recently, deep learning-based methods are widely used for attributed graph clustering. DAEGC is a goal-directed method, which utilizes graph attention encoders for latent information learning and performs competitively in attributed graph clustering. To better aggregate the structure and node attribute information in DAEGC, we employ a lightweight attention mechanism to capture the relationship between nodes and their neighbors, learn hidden representations, and use a self-training module to train the model for optimal results. Extensive experiments show that our proposed method improves DAEGC and achieves superior performance compared with state-of-the-art algorithms in attributed graph clustering. © 2022, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Complex networks

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Handwritten mathematical expression recognition with self-attention 4

Handwritten mathematical expression recognition with self-at...

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4th International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2021

作者： Chi, Xueke Wang, Da-Han Wu, Yuefeng Wu, Yun Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9781450385053

Attention-based encoder-decoder models have made great success on handwritten mathematical expression recognition in recent years. However, this kind of method has the problem of attention drift, because under the local attention mechanism based on RNN, the high similarity between coding features can cause attention confusion. To settle this problem, we propose an encoder-decoder model with self-attention, which captures the global information of the feature map and fuses the local information of the CNN as complementary features. Experiments are conducted on the CROHME2014 and CROHME 2016 competition datasets. The experimental results show that, when only using the official training dataset, the proposed method achieves recognition accuracies of 51.98% and 50.74% on the CROHME2014 and CROHME2016 competition datasets, respectively, which outperforms the other methods significantly. The improvements demonstrate the effectiveness of the self-attention module. © 2021 ACM.

关键词： Signal encoding

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Handwritten Mathematical Expression recognition with Self-Attention 21

Handwritten Mathematical Expression Recognition with Self-At...

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Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence

作者： Xueke Chi Da-Han Wang Yuefeng Wu Yun Wu Fujian Key Laboratory of Pattern Recognition and Image Understanding China and School of Computer and Information Engineering Xiamen University of Technology China

ISBN: (纸本)9781450385053

关键词： handwritten mathematical expression offline recognition non-local self-attention

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Deep Spatial-Temporal Network Based on Residual Networks and Dilated Convolution for Traffic Flow Prediction

Deep Spatial-Temporal Network Based on Residual Networks and...

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IEEE International Conference on Intelligent Transportation Engineering (ICITE)

作者： Liangming Dong Xinxin Zhang Lijuan Liu College of Computer and Information Engineering (Xiamen University of Technology) Fujian Key Laboratory of Pattern Recognition and Image Understanding (Xiamen University of Technology) Xiamen China

As modern cities become more intelligent, traffic flow forecasting is becoming increasingly significant in intelligent transport and urban governance. Accurate traffic forecasting can help the management and the residents to travel, and how to achieve accurate and efficient traffic forecasting is a problem that has been studied by many scholars. With the growth of artificial intelligence and the generation of large-scale traffic data, deep learning can be more effective than traditional probabilistic prediction models in traffic flow forecasting, but there are two more problems to be solved. Firstly, the use of too many levels of convolution often leads to over-fitting. Secondly, the state of traffic in a city can be impacted by external factors such as meteorological conditions, temperature, and wind speed. To solve this problem, we propose an end-to-end neural network model based on the residual network, dilated convolution, and call it STDCN. Convolution and dilated convolution are used in the model to obtain local spatial dependencies and distant spatial dependencies. We designed an external feature extractor to transform the external factors into a ID tensor using the One-Hot Encoding.

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Multi-Task Learning with Deep Dual-Path Network for Facial Attribute recognition 2020

Multi-Task Learning with Deep Dual-Path Network for Facial A...

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Proceedings of the 2020 9th International Conference on Computing and pattern recognition

作者： Xinyu Lai Si Chen Da-Han Wang Shunzhi Zhu School of Computer and Information Engineering Xiamen University of Technology Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology Fujian Key Laboratory of Pattern Recognition and Image Understanding

ISBN: (纸本)9781450387835

Facial attribute recognition is a popular and challenging research topic in computer vision. In the traditional deep learning based attribute recognition methods, the mid-level network features and the differences between attribute groups are not fully explored. To solve the above problem, a deep dual-path network is proposed for facial attribute recognition. In the multi-task learning framework, two sub-networks are employed to respectively extract the features of two attribute groups, i.e., local attributes and global ones, and designed with both different scale images and different depth networks. Furthermore, an adaptive Focal loss penalty scheme is developed to automatically assign weights to handle the class imbalance problem for facial attribute recognition. Experimental results on the challenging CelebA dataset show that the proposed method achieves the better performance than state-of-the-art methods.

关键词： multi-task learning dual-path network Facial attribute recognition attribute groups

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RCFormer : Interactive image Segmentation via Reconstructing Click Vision Transformers

RCFormer : Interactive Image Segmentation via Reconstructing...

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International Joint Conference on Neural Networks (IJCNN)

作者： PanPan Chen DaHan Wang JiaHua Wu Yun Wu YiFan He XuYao Zhang ShunZhi Zhu Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen China Xiamen Key Laboratory of Visual Perception Technology and Applications Reconova Technologies Co.Ltd Xiamen China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Click-based interactive image segmentation intends to segment an object from the background under user click guidance. Recently, Vision Transformer has made significant strides in interactive image segmentation. However, the previous studies 1) overlook the importance of different clicks in terms of their contribution to the segmentation results; and 2) suffer from inconsistency across different feature scales in the multi-scale structure. In this paper, we propose a new interactive segmentation framework, named RCFormer, with two novel components: reconstruct click patch embedding (RCPE) for encoding the importance of clicks, and multi-scale adaptive fusion (MSAF) for the adaptive fusion of feature maps across different scales. RCPE enhances the effectiveness of click interactions by spatially distinguishing the importance of clicks. MSAF adaptively fuses useful spatial information and filters the redundant feature at multi-scales. The experiments on several benchmarks show that our proposed approach achieves state-of-the-art performance. Notably, our method achieves 2.31 NoC@90 on the Berkeley dataset, improving by 8.6% over the previous best results.

关键词： image segmentation Computer vision Filters Fuses Neural networks Network architecture Transformers

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