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arXiv 2021年

作者： Xue, Xizhe Zhang, Haokui Fang, Bei Bai, Zongwen Li, Ying The School of Computer Science National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northwestern Polytechnical University Xi’an China Harbin Institute of Technology Shenzhen China The Key Laboratory of Modern Teaching Technology Ministry of Education Shaanxi Normal University Xi’an China The School of Physics and Electronic Information Yan'an University Yan’an China

Hyperspectral image (HSI) classification has been a hot topic for decides, as hyperspectral images have rich spatial and spectral information and provide strong basis for distinguishing different land-cover objects. Benefiting from the development of deep learning technologies, deep learning based HSI classification methods have achieved promising performance. Recently, several neural architecture search (NAS) algorithms have been proposed for HSI classification, which further improve the accuracy of HSI classification to a new level. In this paper, NAS and Transformer are combined for handling HSI classification task for the first time. Compared with previous work, the proposed method has two main differences. First, we revisit the search spaces designed in previous HSI classification NAS methods and propose a novel hybrid search space, consisting of the space dominated cell and the spectrum dominated cell. Compared with search spaces proposed in previous works, the proposed hybrid search space is more aligned with the characteristic of HSI data, that is, HSIs have a relatively low spatial resolution and an extremely high spectral resolution. Second, to further improve the classification accuracy, we attempt to graft the emerging transformer module on the automatically designed convolutional neural network (CNN) to add global information to local region focused features learned by CNN. Experimental results on three public HSI datasets show that the proposed method achieves much better performance than comparison approaches, including manually designed network and NAS based HSI classification methods. Especially on the most recently captured dataset Houston University, overall accuracy is improved by nearly 6 percentage points. Code is available at: https://***/Cecilia-xue/HyT-NAS. © 2021, CC BY.

关键词： Convolutional neural networks

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Contourlet Refinement Gate Framework for Thermal Spectrum Distribution Regularized Infrared Image Super-Resolution

arXiv

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arXiv 2024年

作者： Zou, Yang Chen, Zhixin Zhang, Zhipeng Li, Xingyuan Ma, Long Liu, Jinyuan Wang, Peng Zhang, Yanning School of Computer Science Northwestern Polytechnical University Xi’an710129 China Graduate School of Information Production and Systems Waseda University Fukuoka8080135 Japan School of Software Dalian University of Technology Dalian116024 China School of Mechanical Engineering Dalian University of Technology Dalian116024 China The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China

Image super-resolution (SR) is a classical yet still active low-level vision problem that aims to reconstruct high-resolution (HR) images from their low-resolution (LR) counterparts, serving as a key technique for image enhancement. Current approaches to address SR tasks, such as transformer-based and diffusion-based methods, are either dedicated to extracting RGB image features or assuming similar degradation patterns, neglecting the inherent modal disparities between infrared and visible images. When directly applied to infrared image SR tasks, these methods inevitably distort the infrared spectral distribution, compromising the machine perception in downstream tasks. In this work, we emphasize the infrared spectral distribution fidelity and propose a Contourlet refinement gate framework to restore infrared modal-specific features while preserving spectral distribution fidelity. Our approach captures high-pass subbands from multi-scale and multi-directional infrared spectral decomposition to recover infrared-degraded information through a gate architecture. The proposed Spectral Fidelity Loss regularizes the spectral frequency distribution during reconstruction, which ensures the preservation of both high- and low-frequency components and maintains the fidelity of infrared-specific features. We propose a two-stage prompt-learning optimization to guide the model in learning infrared HR characteristics from LR degradation. Extensive experiments demonstrate that our approach outperforms existing image SR models in both visual and perceptual tasks while notably enhancing machine perception in downstream tasks. Our code is available at https://***/hey-it-s-me/*** Codes 68T45 Copyright © 2024, The Authors. All rights reserved.

关键词： Infrared imaging

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Continual Learning for Smart City: A Survey

arXiv

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arXiv 2024年

作者： Yang, Li Luo, Zhipeng Zhang, Shiming Teng, Fei Li, Tianrui School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Sichuan Chengdu611756 China

With the digitization of modern cities, large data volumes and powerful computational resources facilitate the rapid update of intelligent models deployed in smart cities. Continual learning (CL) is a novel machine learning paradigm that constantly updates models to adapt to changing environments, where the learning tasks, data, and distributions can vary over time. Our survey provides a comprehensive review of continual learning methods that are widely used in smart city development. The content consists of three parts: 1) Methodology-wise. We categorize a large number of basic CL methods and advanced CL frameworks in combination with other learning paradigms including graph learning, spatial-temporal learning, multi-modal learning, and federated learning. 2) application-wise. We present numerous CL applications covering transportation, environment, public health, safety, networks, and associated datasets related to urban computing. 3) Challenges. We discuss current problems and challenges and envision several promising research directions. We believe this survey can help relevant researchers quickly familiarize themselves with the current state of continual learning research used in smart city development and direct them to future research trends. Copyright © 2024, The Authors. All rights reserved.

关键词： Smart city

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Batch-Based Vehicle Tracking in Smart Cities: A data Fusion and Information Integration Approach

SSRN

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SSRN 2023年

作者： Sun, Zhanbo Huang, Zhihang Hao, Peng Ban, Xuegang Huang, Tianyu School of Transportation and Logistics Southwest Jiaotong University Chengdu China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu China Center for Environmental Research & Technology University of California RiversideCA United States Department of Civil and Environmental Engineering University of Washington SeattleWA United States Civil Aviation Flight University of China Guanghan China

A data fusion and information integration (DFII-VT) framework is proposed to solve the batch-based vehicle matching/tracking problem using heterogeneous fixed-location and mobile sensing data available in smart cities. To make the model more realistic, traffic knowledge such as lane-choice decision, traffic merging, travel time and vehicle characteristics are calibrated using the historical dataset and then integrated into the model. The problem can be formulated as a combinatorial optimization model, and solved using a dynamic programming/Kuhn-Munkres algorithm-based two-step approach. By doing so, the proposed method can obtain individual travel times of the matched data pairs, and these can be directly used to estimate the corridor travel times of individual vehicles. The experimental results show that the fusion of mobile sensing data and fixed-location data yields significantly better results than using single-source data. Significant improvements in matching accuracy are obtained as more traffic information is integrated into the model. Therefore, the method may be considered a framework for integrating the manifold traffic information acquired within smart cities to obtain more accurate matching results and further used to optimize other fine-grained traffic applications, such as estimating vehicle trajectories along arterial corridors, estimating individual vehicle-based fuel consumption/emissions, and helping to infer real-time queuing processes at signalized intersections. The paper also studies some practical issues related to the use of heterogeneous traffic data, such as data errors, and detection failures. © 2023, The Authors. All rights reserved.

关键词： Travel time

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Joint gaze estimation and facial expression for student engagement prediction in collaborative learning

Joint gaze estimation and facial expression for student enga...

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IEEE International Conference on Teaching, Assessment and Learning for engineering (TALE)

作者： Yi Chen Jin Zhou Jing Gao Ge Gao Sai Wang Wei Zhang School of Computer Science Central China Normal University Wuhan P.R.China School of Computer Science Wuhan University Wuhan P.R.China National Engineering Laboratory of Educational Big Data Application Technology Central China Normal University Wuhan P.R.China

ISBN: (纸本)9781665436885

Estimating and improving students' engagement in a collaborative learning environment is an important component in the field of learning research. Collaborative learning is a strategy of learning activities employed by small groups in which cooperative learning behaviors are closely related to other members or objects in the group. Researchers showed that students who are actively involved in class learn more. Therefore, gaze behavior and facial expression are important nonverbal indicators in cooperative learning environments. In this paper, we proposed a multimodal deep neural network (MDNN) to solve the engagement prediction problem in collaborative learning. We combined facial expression and gaze direction as individual streams of MDNN to predict engagement levels in collaborative learning environments. Our multi-modal solution was evaluated in a real collaborative environment with a significant accuracy of 74%. The results show that the model can accurately predict students' performance in the collaborative learning environment.

关键词： Deep learning Neural networks Education Collaboration Estimation Predictive models Collaborative work

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Intra-Person Camera Adversarial for Intra-Camera Supervised Person Re-Identification

Intra-Person Camera Adversarial for Intra-Camera Supervised ...

引用

International Conference on Automatic Face and Gesture Recognition

作者： Ruochen Tang Xun Gong School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu China

ISBN: (数字)9798350394948

ISBN: (纸本)9798350394955

Re-identification (ReID) of individuals across different cameras is a challenging task due to the high-quality large-scale datasets required for the model. Intra-camera supervised (ICS) person re-identification has been proposed to address the high cost of annotating large-scale datasets, but reducing the gap between camera domains remains a major challenge. Current approaches use intra- and inter-camera learning with contrastive learning performed separately in both phases. However, the effect of features from the same person under different cameras on the model during inter-camera learning, and the fine-grained characteristic where the same person can be classified into multiple classes based on the camera labels, still require further research. To address this issue, we propose a Camera-Based Contrastive Learning (CBCL) method that moves features away from their respective cameras and closer to other cameras to reduce domain gaps. We also introduce an Intra-Person Camera Adversarial (IPCA) loss that effectively utilizes fine-grained characteristics of person re-identification and improve IPCA by introducing camera labels to obtain IPCA_2 which achieves better model recognition performance than IPCA alone. Extensive experiments on multiple datasets demonstrate that our method outperforms existing methods and is comparable to fully-supervised methods.

关键词： Costs Scalability Face recognition Contrastive learning Gesture recognition Cameras Robustness

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Pairwise relation learning for semi-supervised gland segmentation

arXiv

引用

arXiv 2020年

作者： Xie, Yutong Zhang, Jianpeng Liao, Zhibin Shen, Chunhua Verjans, Johan Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xian710072 China University of Adelaide Australia South Australian Health and Medical Research Institute Adelaide Australia Research & Development Institute Northwestern Polytechnical University in Shenzhen Shenzhen518057 China

Accurate and automated gland segmentation on histology tissue images is an essential but challenging task in the computer-aided diagnosis of adenocarcinoma. Despite their prevalence, deep learning models always require a myriad number of densely annotated training images, which are difficult to obtain due to extensive labor and associated expert costs related to histology image annotations. In this paper, we propose the pairwise relation-based semi-supervised (PRS2) model for gland segmentation on histology images. This model consists of a segmentation network (S-Net) and a pairwise relation network (PR-Net). The S-Net is trained on labeled data for segmentation, and PR-Net is trained on both labeled and unlabeled data in an unsupervised way to enhance its image representation ability via exploiting the semantic consistency between each pair of images in the feature space. Since both networks share their encoders, the image representation ability learned by PR-Net can be transferred to S-Net to improve its segmentation performance. We also design the object-level Dice loss to address the issues caused by touching glands and combine it with other two loss functions for S-Net. We evaluated our model against five recent methods on the GlaS dataset and three recent methods on the CRAG dataset. Our results not only demonstrate the effectiveness of the proposed PR-Net and object-level Dice loss, but also indicate that our PRS2 model achieves the state-of-the-art gland segmentation performance on both benchmarks. Copyright © 2020, The Authors. All rights reserved.

关键词： Histology

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Study on the Influence of Interaction Style on Learners

Study on the Influence of Interaction Style on Learners

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IEEE International Conference on Teaching, Assessment and Learning for engineering (TALE)

作者： Yi Chen Yidan Wang Jin Zhou Jing Gao Ge Gao Wei Zhang School of Computer Science Central China Normal University Wuhan P.R.China School of Computer Science Wuhan University Wuhan P.R.China National Engineering Laboratory of Educational Big Data Application Technology Central China Normal University Wuhan P.R.China

ISBN: (纸本)9781665436885

High-quality mobile learning resources are an important support for mobile learning. Interactivity is an important feature of mobile learning, which directly affects the effect of the interaction between learners and learning resources, and is a key indicator for evaluating the quality of learning resources. This article uses brain-computer interface technology to explore the changes in learning attention and learning effectiveness of mobile learners in different presentation forms and interactive methods of learning resources, including plain text, video and interactive methods represented by dubbing. Research shows that: First, different forms of knowledge presentation and interaction have a significant impact on attention. Second, in the interactive mode represented by dubbing, the learner's attention is higher and the learning effect is the best. Therefore, when designing mobile learning resources in the future, the inter activity of resources should be considered to maintain learners' higher learning attention and achieve better mobile learning effects.

关键词： Learning systems Education Psychology Media Streaming media Mobile learning Brain-computer interfaces

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Pansharpening via Frequency-Aware Fusion Network with Explicit Similarity Constraints

arXiv

引用

arXiv 2022年

作者： Xing, Yinghui Zhang, Yan He, Houjun Zhang, Xiuwei Zhang, Yanning National Engineering Laboratory for Integrated Aerospace-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi’an710072 China Research Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518057 China Information Center of Yellow River Conservancy Commission Henan Zhengzhou China

The process of fusing a high spatial resolution (HR) panchromatic (PAN) image and a low spatial resolution (LR) multispectral (MS) image to obtain an HRMS image is known as pansharpening. With the development of convolutional neural networks, the performance of pansharpening methods has been improved, however, the blurry effects and the spectral distortion still exist in their fusion results due to the insufficiency in details learning and the frequency mismatch between MS and PAN. Therefore, the improvement of spatial details at the premise of reducing spectral distortion is still a challenge. In this paper, we propose a frequency-aware fusion network (FAFNet) together with a novel high-frequency feature similarity loss to address above mentioned problems. FAFNet is mainly composed of two kinds of blocks, where the frequency aware blocks aim to extract features in the frequency domain with the help of discrete wavelet transform (DWT) layers, and the frequency fusion blocks reconstruct and transform the features from frequency domain to spatial domain with the assistance of inverse DWT (IDWT) layers. Finally, the fusion results are obtained through a convolutional block. In order to learn the correspondence, we also propose a high-frequency feature similarity loss to constrain the HF features derived from PAN and MS branches, so that HF features of PAN can reasonably be used to supplement that of MS. Experimental results on three datasets at both reduced- and full-resolution demonstrate the superiority of the proposed method compared with several state-of-the-art pansharpening models. The codes are available at https://***/YinghuiXing/FAFNet. Copyright © 2022, The Authors. All rights reserved.

关键词： Remote sensing

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$\mathsf{NCF}$NCF: A Neural Context Fusion Approach to Raw Mobility Annotation

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IEEE Transactions on Mobile Computing 2020年第1期21卷 226-238页

作者： Renjun Hu Jingbo Zhou Xinjiang Lu Hengshu Zhu Shuai Ma Hui Xiong SKLSDE Lab Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Business Intelligence Lab Baidu Research National Engineering Laboratory of Deep Learning Technology and Application Beijing China Talent Intelligence Center Baidu Inc. Beijing China Management Science and Information Systems Department Rutgers Business School Rutgers University Newark NJ USA

Understanding human mobility patterns at the point-of-interest (POI) scale plays an important role in enhancing business intelligence in mobile environments. While large efforts have been made in this direction, most studies simply utilize POI check-ins to mine the concerned mobility patterns, the effectiveness of which is usually hindered due to data sparsity. To obtain better POI-based human mobility for mining, in this paper, we strive to directly annotate the POIs associated with raw user-generated mobility records. We propose a neural context fusion approach which integrates various context factors in people's POI-visiting behaviors. Our approach evaluates the preference and transition factors via representation learning. Notably, we incorporate an attention mechanism to deal with the randomized transitions in raw mobility. The domain knowledge factors, i.e., distance, time and popularity, remain effective and our approach further includes them from a data-driven perspective. Factors are automatically fused with a feed-forward neural network. Furthermore, we exploit a multi-head architecture to enhance the model expressiveness. Using two real-life data sets, we conduct our experimental study and find that our approach consistently outperforms the state-of-the-art baselines by at least 32 percent in accuracy. Besides, we demonstrate the utility of the obtained POI-based human mobility with a POI recommendation example.

关键词： Annotations Trajectory Mobile computing Computer architecture Business intelligence Neural networks Semantics

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