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Journal of Network Intelligence 2023年第2期8卷 432-447页

作者： Chen, Yong Xie, Xiao-Zhu Weng, Wei Zhang, Shan-Dan Li, Tong College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China College of Computer and Information Engineering Xiamen University of Technology Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China

Graph neural network (GNN), as a powerful method for graph representation, has attracted extensive research interest. Recently, Graph Convolutional Network (GCN) and Graph Attention Network (GAT) have shown superior performance on graph node classification and are considered as the most promising frameworks. Both GAT and GCN use a weighting mechanism in the information aggregation process. However, GCN is weighted by topological structure, while the node content is still unemployed. GAT is weighted by node content, while the topological structure remains being ignored. These weighting mechanism, which does not make full use of topology and node content, is not conducive to graph representation. In this paper, we propose a "Collaborative Weighting For Graph Convolutional Network" (CWGCN) that combines the advantages of GCN and GAT. Specifically, its weighting mechanism not only considers the topology of the graph, but also the node content. With this weighting method for representation learning, CWGCN achieves encouraging performance on a number of citation datasets in node classification. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Classification (of information)

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EfficientFCN: Holistically-Guided Decoding for Semantic Segmentation 1

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16th European Conference on computer vision, ECCV 2020

作者： Liu, Jianbo He, Junjun Zhang, Jiawei Ren, Jimmy S. Li, Hongsheng CUHK-SenseTime Joint Laboratory The Chinese University of Hong Kong Shatin Hong Kong Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China SenseTime Research Beijing China

ISBN: (数字)9783030585747

ISBN: (纸本)9783030585730

Both performance and efficiency are important to semantic segmentation. State-of-the-art semantic segmentation algorithms are mostly based on dilated Fully Convolutional Networks (dilatedFCN), which adopt dilated convolutions in the backbone networks to extract high-resolution feature maps for achieving high-performance segmentation performance. However, due to many convolution operations are conducted on the high-resolution feature maps, such dilatedFCN-based methods result in large computational complexity and memory consumption. To balance the performance and efficiency, there also exist encoder-decoder structures that gradually recover the spatial information by combining multi-level feature maps from the encoder. However, the performances of existing encoder-decoder methods are far from comparable with the dilatedFCN-based methods. In this paper, we propose the EfficientFCN, whose backbone is a common ImageNet pretrained network without any dilated convolution. A holistically-guided decoder is introduced to obtain the high-resolution semantic-rich feature maps via the multi-scale features from the encoder. The decoding task is converted to novel codebook generation and codeword assembly task, which takes advantages of the high-level and low-level features from the encoder. Such a framework achieves comparable or even better performance than state-of-the-art methods with only 1/3 of the computational cost. Extensive experiments on PASCAL Context, PASCAL VOC, ADE20K validate the effectiveness of the proposed EfficientFCN. © 2020, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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Automatic object segmentation from large scale 3D urban point clouds through manifold embedded mode seeking 11

Automatic object segmentation from large scale 3D urban poin...

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19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11

作者： Yu, Zhiding Xu, Chunjing Liu, Jianzhuang Au, Oscar C. Tang, Xiaoou Shenzhen Key Laboratory for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Department of Information Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781450306164

This paper presents a system that can automatically segment objects in large scale 3D point clouds obtained from urban ranging images. The system consists of three steps: The first one involves a ground detection process that can detect relatively complex terrain and separate it from other objects. The second step superpixelizes the remaining objects to speed up the segmentation process. In the final step, a manifold embedded mode seeking method is adopted to segment the point clouds. Even though the segmentation of urban objects is a challenging problem in terms of accuracy and problem scale, our system can efficiently generate very good segmentation results. The proposed manifold learning effectively improves the segmentation performance due to the fact that continuous artificial objects often have manifold-like structures. Copyright 2011 ACM.

关键词： Image segmentation

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Face-sketch learning with human sketch-drawing order enforcement

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Science China(Information Sciences) 2020年第11期63卷 298-311页

作者： Liang CHANG Lihua JIN Lifen WENG Wentao CHAO Xuguang WANG Xiaoming DENG Qiulei DONG School of Artificial Intelligence Beijing Normal University Department of Design Art Xiamen University of Technology Department of Automation North China Electric Power University Beijing Key Laboratory of Human Computer Interactions Institute of Software Chinese Academy of Sciences National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences

Dear editor,Although face-sketch synthesis generates a sketch from a given face photo automatically [1], it is an open research problem in computer vision [2–4]. Recently, several deep neural network (DNN)methods for... 详细信息

关键词： face sketch synthesis deep neural network order enforcement image synthesis generative adversarial network

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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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3D object retrieval with semantic attributes 11

3D object retrieval with semantic attributes

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19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11

作者： Gong, Boqing Liu, Jianzhuang Wang, Xiaogang Tang, Xiaoou Department of Information Engineering Chinese University of Hong Kong Hong Kong Department of Electronic Engineering Chinese University of Hong Kong Hong Kong Shenzhen Key Laboratory for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (纸本)9781450306164

Humans are capable of describing objects using attributes, such as "the object looks circular and is man-made". Motivated by these high-level descriptions, we build a user-friendly 3D object retrieval system, where the user can browse the database and search for targeted objects using semantic attributes. The main advantage of our system is that it does not require the user to find or sketch a 3D object as the query for 3D object retrieval. Besides, to the best of our knowledge, our system has obtained the best retrieval performance on three popular benchmarks. Copyright 2011 ACM.

关键词： Semantics

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Efficient Image Super-Resolution Using Vast-Receptive-Field Attention 17th

Efficient Image Super-Resolution Using Vast-Receptive-Field ...

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17th European Conference on computer vision, ECCV 2022

作者： Zhou, Lin Cai, Haoming Gu, Jinjin Li, Zheyuan Liu, Yingqi Chen, Xiangyu Qiao, Yu Dong, Chao ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Shanghai AI Laboratory Shanghai China The University of Sydney Sydney Australia University of Macau Zhuhai China

ISBN: (纸本)9783031250620

The attention mechanism plays a pivotal role in designing advanced super-resolution (SR) networks. In this work, we design an efficient SR network by improving the attention mechanism. We start from a simple pixel attention module and gradually modify it to achieve better super-resolution performance with reduced parameters. The specific approaches include: (1) increasing the receptive field of the attention branch, (2) replacing large dense convolution kernels with depthwise separable convolutions, and (3) introducing pixel normalization. These approaches paint a clear evolutionary roadmap for the design of attention mechanisms. Based on these observations, we propose VapSR, the Vast-receptive-field Pixel attention network. Experiments demonstrate the superior performance of VapSR. VapSR outperforms the present lightweight networks with even fewer parameters. And the light version of VapSR can use only 21.68% and 28.18% parameters of IMDB and RFDN to achieve similar performances to those networks. The code and models are available at https://***/zhoumumu/VapSR. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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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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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

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.

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

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Stain-Adaptive Self-Supervised Learning for Histopathology Image Analysis

arXiv

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arXiv 2022年

作者： Ye, Hai-Li Wang, Da-Han Department of Computer and Information Engineering Xiamen University of Technology Xiamen361000 China Fujian Provincial Key Laboratory of Pattern Recognition and Image Understanding Xiamen361000 China

It is commonly recognized that color variations caused by differences in stains is a critical issue for histopathology image analysis. Existing methods adopt color matching, stain separation, stain transfer or the combination of them to alleviate the stain variation problem. In this paper, we propose a novel Stain-Adaptive Self-Supervised Learning(SASSL) method for histopathology image analysis. Our SASSL integrates a domain-adversarial training module into the SSL framework to learn distinctive features that are robust to both various transformations and stain variations. The proposed SASSL is regarded as a general method for domain-invariant feature extraction which can be flexibly combined with arbitrary downstream histopathology image analysis modules (e.g. nuclei/tissue segmentation) by fine-tuning the features for specific downstream tasks. We conducted experiments on publicly available pathological image analysis datasets including the PANDA, BreastPathQ, and CAMELYON16 datasets, achieving the state-of-the-art performance. Experimental results demonstrate that the proposed method can robustly improve the feature extraction ability of the model, and achieve stable performance improvement in downstream tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Image analysis

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