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IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS 2022年 3卷 126-136页

作者： Wang, Hu Chen, Hao Wu, Qi Ma, Congbo Li, Yidong Univ Adelaide Sch Comp Sci Adelaide SA 5005 Australia Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China

The control of traffic signals is fundamental and critical to alleviate traffic congestion in urban areas. However, it is challenging since traffic dynamics are complicated in real-world scenarios. Because of the high complexity of the optimisation problem for modeling the traffic, experimental settings of existing works are often inconsistent. Moreover, it is not trivial to control multiple intersections properly in real complex traffic scenarios due to its vast state and action space. Failing to take intersection topology relations into account also results in inferior solutions. To address these issues, in this work we carefully design our settings and propose a new dataset including both synthetic and real traffic data in more complex scenarios. Additionally, we propose a novel baseline model with strong performance. It is based on deep reinforcement learning with an encoder-decoder structure: an edge-weighted graph convolutional encoder to excavate multi-intersection relations;and an unified structure decoder to jointly model multiple junctions in a comprehensive manner, which significantly reduces the number of the model parameters. By doing so, the proposed model is able to effectively deal with the multi-intersection traffic control optimisation problem. Models are trained/tested on both synthetic and real maps and traffic data with the Simulation of Urban Mobility (SUMO) simulator. Experimental results show that the proposed model surpasses multiple competitive methods. The traffic data and the code can be found at https://***/JPdU1.

关键词： Optimization Junctions Switches Reinforcement learning Convolution Markov processes Urban areas Traffic control optimisation multiple intersections deep reinforcement learning encoder-decoder structure

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Ship Segmentation via encoder-decoder Network With Global Attention in High-Resolution SAR Images

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Li, Jichao Gou, Shuiping Li, Ruimin Chen, Jia-Wei Sun, Xiaolong Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Peoples R China Xidian Univ Acad Adv Interdisciplinary Res Xian 710071 Shaanxi Peoples R China China Elect Technol Grp Corp 20th Res Inst Xian 710068 Shaanxi Peoples R China

Ship detection in the synthetic aperture radar (SAR) image is of great significance in the fields of military and coastal defense. Most ship detection methods are designed based on the object detection framework, which can only provide the vertices' coordinates of the bounding box covering the ship targets but cannot provide more detailed contour information. Target segmentation can further explore the shape and edge information of the objects, which can be used as a blazing novel means for automatic object detection. In this letter, a 3-D atrous encoder-decoder neural network with global attention modules (GAM-EDNet) is proposed to achieve ship segmentation in SAR images. The encoder-decoder structure with atrous convolution is developed as the network body to fully exploit the structural information of the ship targets with various sizes. To increase the structural information of the single-polarization SAR images, a 3-D image cube is designed as the input of the GAM-EDNet. A global attention module is proposed to further improve the segmentation performance by integrating the high-level semantic features with the low-level location features. Besides, an SAR ship segmentation dataset (SAR-HR4) is built to evaluate the segmentation performance, and the experimental results show that the proposed GAM-EDNet achieves better performance than other state-of-the-art methods.

关键词： Marine vehicles Image segmentation Radar polarimetry Semantics Synthetic aperture radar Feature extraction Wavelet transforms encoder-decoder structure global attention module high-resolution synthetic aperture radar (SAR) images ship segmentation

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Semantic Segmentation using Light Attention Mechanism 15

Semantic Segmentation using Light Attention Mechanism

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15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 15th International Conference on Computer Vision Theory and Applications (VISAPP)

作者： Hiramatsu, Yuki Hotta, Kazuhiro Meijo Univ Nagoya Aichi Japan

ISBN: (纸本)9789897584022

Semantic segmentation using convolutional neural networks (CNN) can be applied to various fields such as automatic driving. Semantic segmentation is pixel-wise class classification, and various methods using CNN have been proposed. We introduce a light attention mechanism to the encoder-decoder network. The network that introduced a light attention mechanism pays attention to features extracted during training, emphasizes the features judged to be effective for training and suppresses the features judged to be irrelevant for each pixel. As a result, training can be performed by focusing on only necessary features. We evaluated the proposed method using the CamVid dataset and obtained higher accuracy than conventional segmentation methods.

关键词： Semantic Segmentation Attention Mechanism encoder-decoder structure

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ICANet: A LIGHTWEIGHT INCREASING CONTEXT AIDED NETWORK FOR REAL-TIME IMAGE SEMANTIC SEGMENTATION

ICANet: A LIGHTWEIGHT INCREASING CONTEXT AIDED NETWORK FOR R...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Chen, Lei Dai, Huhe Zheng, Yuan Inner Mongolia Univ Coll Elect Informat Engn Hohhot Peoples R China Inner Mongolia Univ Coll Comp Sci Hohhot Peoples R China

ISBN: (纸本)9781665468916

Although existing real-time segmentation methods have reduced the network parameters, they still suffer from slow inference speed. To alleviate this problem, we propose a novel lightweight image segmentation method, called Increasing Context Aided Network (ICANet), which brings a significant increase in inference speed while maintaining competitive segmentation accuracy. To do this, we elaborately design both encoder and decoder. Considering that a large reduction in channel numbers brings poor performance, we propose a Inverted Depthwise Separable convolution block (IDS block) that enriches semantic information in channel and spatial dimensions. By stacking several such IDS blocks, we build an efficient encoder that can capture an increasing context by using different dilation rates in IDS blocks to further improve accuracy. Besides, we present a simple decoder that adopts a bottom-up pathway to effectively fuse multiscale feature maps from encoder. Extensive experiments show our ICANet achieves a SOTA balance between accuracy, speed and parameters.

关键词： Image semantic segmentation real-time lightweight network encoder-decoder structure

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SSLIDE: SOUND SOURCE LOCALIZATION FOR INDOORS BASED ON DEEP LEARNING

SSLIDE: SOUND SOURCE LOCALIZATION FOR INDOORS BASED ON DEEP ...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Wu, Yifan Ayyalasomayajula, Roshan Bianco, Michael J. Bharadia, Dinesh Gerstoft, Peter Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (纸本)9781728176055

This paper presents SSLIDE, Sound Source Localization for Indoors using DEep learning, which applies deep neural networks (DNNs) with encoder-decoder structure to localize sound sources with random positions in a continuous space. The spatial features of sound signals received by each microphone are extracted and represented as likelihood surfaces for the sound source locations in each point. Our DNN consists of an encoder network followed by two decoders. The encoder obtains a compressed representation of the input likelihoods. One decoder resolves the multipath caused by reverberation, and the other decoder estimates the source location. Experiments based on both the simulated and experimental data show that our method can not only outperform multiple signal classification (MUSIC), steered response power with phase transform (SRP-PHAT), sparse Bayesian learning (SBL), and a competing convolutional neural network (CNN) approach in the reverberant environment but also achieve a good generalization performance.

关键词： Indoor sound source localization multipath encoder-decoder structure deep neural networks

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MatTrip: Multi-functional Attention-based Neural Network for Semantic Travel Route Recommendation

MatTrip: Multi-functional Attention-based Neural Network for...

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IEEE International Conference on Web Services (ICWS) / IEEE World Congress on Services (IEEE SERVICES)

作者： Yang, Chenxiao Zhang, Jiale Gao, Xiaofeng Chen, Guihai Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence Dept Comp Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781665416818

Travel route recommendation aims to recommend a sequence of point of interests (POIs) for visitors based on their personal interests. Previous studies utilize user interest features and POI spatial information to provide travel route recommendation service. However, most of them fail to consider the implicit information in user traveling patterns, which is the key to improve recommendation quality. Additionally, few deep learning based travel route recommendation systems provide comprehensive trip planning functionalities, which is critical to improve the user experience. To alleviate these two problems, we propose a multi-functional attention-based neural network for route recommendation (named MatTrip). We first introduce an encoder-decoder structure with a novel dual bi-directional LSTM encoder as the sequence generation model to learn other users' traveling records and generates a semantic travel route based on user preference and geographical features of start/end POI. Next, multiple user-specific functionalities are supported in MatTrip by grid beam search. The functionalities include weather dependency, POI opening hours, restricted sequence length, mandatory POIs, and dynamic route revision. In addition, MatTrip adopts an online learning approach to learn from user deviation behaviors to improve recommendation performance. Experiments on two real-world datasets show that our model achieves a 20.98% improvement in performance, compared with state-of-arts.

关键词： Travel Route Recommendation POI Recommendation encoder-decoder structure Grid Beam Search

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An Integrated Deep Neural Network Approach for Large-Scale Water Quality Time Series Prediction

An Integrated Deep Neural Network Approach for Large-Scale W...

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IEEE International Conference on Systems, Man and Cybernetics (SMC)

作者： Dong, QuanXi Lin, YongZhe Bi, Jing Yuan, Haitao Beijing Univ Technol Fac Informat Technol Beijing Peoples R China Beijing Jiaotong Univ Sch Software Engn Beijing Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

ISBN: (纸本)9781728145693

The prediction of water quality has great significance for the management of water environment and the protection of water resources. Traditional water quality prediction methods are relatively simple, and linear models are often used to predict water quality. However, such models limit the accuracy of prediction and lack the analysis of nonlinear characteristics of water quality. In addition, due to the complex water environment, the water quality time series has large noise, which makes it difficult for traditional models to effectively predict water quality indicators under complex environmental conditions. To solve this problem, this work proposes an integrated prediction method that combines Savitzky-Golay filter with Long Short-Term Memory (LSTM)-based encoder-decoder neural network to predict water quality at the next time interval. In this approach, the water quality time series is first smoothed by Savitzky-Golay filter, and LSTM can extract valid information from complex time series. Based on them, an integrated model is for the first time established and can well characterize statistical characteristics. Experimental results demonstrate that it achieves better prediction results than some typical prediction methods.

关键词： Water quality prediction LSTM encoder-decoder structure Savitzky-Golay filter water environment

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Design of oral English teaching model based on multi-modal perception of the Internet of Things and improved conventional neural networks

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PEERJ COMPUTER SCIENCE 2023年 9卷 e1503页

作者： Qin, Haitao Hubei Normal Univ Coll Foreign Studies Huangshi Hubei Peoples R China

Oral English instruction plays a pivotal role in educational endeavors. The emergence of online teaching in response to the epidemic has created an urgent demand for a methodology to evaluate and monitor oral English instruction. In the post-epidemic era, distance learning has become indispensable for educational pursuits. Given the distinct teaching modality and approach of oral English instruction, it is imperative to explore an intelligent scoring technique that can effectively oversee the content of English teaching. With this objective in mind, we have devised a scoring approach for oral English instruction based on multi-modal perception utilizing the Internet of Things (IoT). Initially, a trained convolutional neural network (CNN) model is employed to extract and quantify visual information and audio features from the IoT, reducing them to a fixed dimension. Subsequently, an external attention model is proposed to compute spoken English and image characteristics. Lastly, the content of English instruction is classified and graded based on the quantitative attributes of oral dialogue. Our findings illustrate that our scoring model for oral English instruction surpasses others, achieving the highest rankings and an accuracy of 88.8%, outperforming others by more than 2%.

关键词： Oral English teaching Multi-modal perception encoder-decoder structure CNN

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Multi-path connected network for medical image segmentation

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2020年 71卷 102852-102852页

作者： Wang, Dan Hu, Guoqing Lyu, Chengzhi South China Univ Technol Sch Mech & Automot Engn Guangzhou Peoples R China

In recent years, deep learning has been successfully applied to medical image segmentation. However, as the network extends deeper, the consecutive downsampling operations will lead to more loss of spatial information. In addition, the limited data and diverse targets increase the difficulty for medical image segmentation. To address these issues, we propose a multi-path connected network (MCNet) for medical segmentation problems. It integrates multiple paths generated by pyramid pooling into the encoding phase to preserve semantic information and spatial details. We utilize multi-scale feature extractor block (MFE block) in the encoder to obtain large and multi-scale receptive fields. We evaluated MCNet on three medical datasets with different image modalities. The experimental results show that our method achieves better performance than the state-of-the-art approaches. Our model has strong feature learning ability and is robust to capture different scale targets. It can achieve satisfactory results while using only 0.98 million (M) parameters. (c) 2020 Elsevier Inc. All rights reserved.

关键词： Medical image segmentation Multi-path connections Convolutional neural networks encoder-decoder structure

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Progressive Guidance Edge Perception Network for Semantic Segmentation of Remote-Sensing Images

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷

作者： Pan, Shaoming Tao, Yulong Chen, Xiaoshu Chong, Yanwen Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China

Remarkable improvements have been seen in the semantic segmentation of remote-sensing images. As an effective structure to aggregate shallow information and deep information, encoder-decoder structure has been widely used in many state-of-the-art models, but it possesses two drawbacks that have not been fully addressed. On the one hand, encoder-decoder structure fuses the features obtained from shallow and deep layers directly;despite harvesting some detailed information, it also brings in noisy features owing to the poor discriminant ability of the shallow layers. On the other hand, existing encoder-decoder structure merely fuses the high-level information generated by the last layer of encoder once, which neglects its guidance ability to the feature aggregation process in the decoder. In this letter, we first propose an edge perception module (EPM) to eliminate the noisy features in the shallow information, as well as enhance features' structural information. And then, we generate the most suitable guidance information adaptively for different stages in the decoder through high-level information module (HIM). Finally, we apply the guidance information to achieve feature aggregation in the feature aggregation module (FAM). Combined with EPM, HIM, and FAM, our proposed model achieves 89.5% overall accuracy (OA) on the challenging ISPRS Vaihingen test set, which is the new state-of-the-art in the semantic segmentation of remote-sensing images.

关键词： Semantics Convolution Noise measurement Remote sensing Image segmentation Feature extraction Decoding encoder-decoder structure high-level information noisy features remote-sensing images semantic segmentation

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