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Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects

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arXiv 2021年

作者： Ahmad, Muhammad Shabbir, Sidrah Roy, Swalpa Kumar Hong, Danfeng Wu, Xin Yao, Jing Khan, Adil Mehmood Mazzara, Manuel Distefano, Salvatore Chanussot, Jocelyn Department of Computer Science National University of Computer and Emerging Sciences Islamabad Chiniot-Faisalabad Campus Chiniot35400 Pakistan Dipartimento di Matematica e Informatica-MIFT University of Messina Messina98121 Italy Pakistan The Department of Computer Science and Engineering Jalpaiguri Government Engineering College West Bengal 735102 India The Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Fractional Signals and Systems Beijing100081 China The Institute of Data Science and Artificial Intelligence Innopolis University Innopolis 420500 Russia Institute of Software Development and Engineering Innopolis University Innopolis420500 Russia The Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-Lab Grenoble38000 France

Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods. In the last few years, Deep Learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic. Primarily, we will encapsulate the main challenges of traditional machine learning for HSIC and then we will acquaint the superiority of DL to address these problems. This survey breakdown the state-of-the-art DL frameworks into spectral-features, spatial-features, and together spatial-spectral features to systematically analyze the achievements (future research directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that DL requires a large number of labeled training examples whereas acquiring such a number for HSIC is challenging in terms of time and cost. Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines. © 2021, CC BY.

关键词： Surveys

Learning with noisy labels for sentence-level sentiment classification

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arXiv 2019年

作者： Wang, Hao Liu, Bing Li, Chaozhuo Yang, Yan Li, Tianrui School of Information Science and Technology Southwest Jiaotong University Department of Computer Science University of Illinois at Chicago State Key Lab of Software Development Environment Beihang University

Deep neural networks (DNNs) can fit (or even over-fit) the training data very well. If a DNN model is trained using data with noisy labels and tested on data with clean labels, the model may perform poorly. This paper studies the problem of learning with noisy labels for sentence-level sentiment classification. We propose a novel DNN model called NETAB (as shorthand for convolutional neural NETworks with AB-networks) to handle noisy labels during training. NETAB consists of two convolutional neural networks, one with a noise transition layer for dealing with the input noisy labels and the other for predicting 'clean' labels. We train the two networks using their respective loss functions in a mutual reinforcement manner. Experimental results demonstrate the effectiveness of the proposed model. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

Potential Indicator for Continuous Emotion Arousal by Dynamic Neural Synchrony

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arXiv 2025年

作者： Pan, Guandong Wu, Zhaobang Yang, Yaqian Wang, Xin Liu, Longzhao Zheng, Zhiming Tang, Shaoting School of Computer Science and Engineering Beihang University Beijing100191 China Institute of Artificial Intelligence Beihang University Beijing100191 China Key laboratory of Mathematics Informatics and Behavioral Semantics Beihang University Beijing100191 China Institute of Medical Artificial Intelligence Binzhou Medical University Yantai264003 China Zhongguancun Laboratory Beijing100094 China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang University Beijing100191 China PengCheng Laboratory Shenzhen518055 China State Key Lab of Software Development Environment Beihang University Beijing100191 China

The need for automatic and high-quality emotion annotation is paramount in applications such as continuous emotion recognition and video highlight detection, yet achieving this through manual human annotations is challenging. Inspired by inter-subject correlation (ISC) utilized in neuroscience, this study introduces a novel Electroencephalography (EEG) based ISC methodology that leverages a single-electrode and feature-based dynamic approach. Our contributions are three folds: Firstly, we reidentify two potent emotion features suitable for classifying emotions—first-order difference (FD) an differential entropy (DE). Secondly, through the use of overall correlation analysis, we demonstrate the heterogeneous synchronized performance of electrodes. This performance aligns with neural emotion patterns established in prior studies, thus validating the effectiveness of our approach. Thirdly, by employing a sliding window correlation technique, we showcase the significant consistency of dynamic ISCs across various features or key electrodes in each analyzed film clip. Our findings indicate the method’s reliability in capturing consistent, dynamic shared neural synchrony among individuals, triggered by evocative film stimuli. This underscores the potential of our approach to serve as an indicator of continuous human emotion arousal. The implications of this research are significant for advancements in affective computing and the broader neuroscience field, suggesting a streamlined and effective tool for emotion analysis in real-world applications. © 2025, CC BY-NC-SA.

关键词： Neurons

A split-and-recombine approach for follow-up query analysis

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arXiv 2019年

作者： Liu, Qian Chen, Bei Liu, Haoyan Lou, Jian-Guang Fang, Lei Zhou, Bin Zhang, Dongmei State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China State Key Lab of Software Development Environment Beihang University China Microsoft Research Beijing China

Context-dependent semantic parsing has proven to be an important yet challenging task. To leverage the advances in context-independent semantic parsing, we propose to perform follow-up query analysis, aiming to restate context-dependent natural language queries with contextual information. To accomplish the task, we propose STAR, a novel approach with a well-designed two-phase process. It is parser-independent and able to handle multifarious follow-up scenarios in different domains. Experiments on the FollowUp dataset show that STAR outperforms the state-of-the-art baseline by a large margin of nearly 8%. The superiority on parsing results verifies the feasibility of follow-up query analysis. We also explore the extensibility of STAR on the SQA dataset, which is very promising. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

DPA-2: a large atomic model as a multi-task learner

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arXiv 2023年

作者： Zhang, Duo Liu, Xinzijian Zhang, Xiangyu Zhang, Chengqian Cai, Chun Bi, Hangrui Du, Yiming Qin, Xuejian Peng, Anyang Huang, Jiameng Li, Bowen Shan, Yifan Zeng, Jinzhe Zhang, Yuzhi Liu, Siyuan Li, Yifan Chang, Junhan Wang, Xinyan Zhou, Shuo Liu, Jianchuan Luo, Xiaoshan Wang, Zhenyu Jiang, Wanrun Wu, Jing Yang, Yudi Yang, Jiyuan Yang, Manyi Gong, Fu-Qiang Zhang, Linshuang Shi, Mengchao Dai, Fu-Zhi York, Darrin M. Liu, Shi Zhu, Tong Zhong, Zhicheng Lv, Jian Cheng, Jun Jia, Weile Chen, Mohan Ke, Guolin Weinan, E. Zhang, Linfeng Wang, Han AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of Sciences Ningbo315201 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Chemistry Princeton University PrincetonNJ08540 United States College of Chemistry and Molecular Engineering Peking University Beijing100871 China Yuanpei College Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Key Laboratory for Quantum Materials of Zhejiang Province Department of Physics School of Science Westlake University Zhejiang Hangzhou310030 China Atomistic Simulations Italia

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi-task learning

A physics-constrained deep learning framework for actual evapotranspiration estimation using ground station observations and remote sensing data

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environmental Modelling & software 2025年

作者： Lingyue Wang Ping Hu Hongwei Zheng Jie Bai Ying Liu Xingwen Cao Olaf Hellwich Tie Liu Anming Bao Xi Chen State Key Laboratory of Desert and Oasis Ecology Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences Urumqi 830011 China University of Chinese Academy of Sciences Beijing 100049 China Research Center for Ecology and Environment of Central Asia Chinese Academy of Sciences Urumqi 830011 China Key Laboratory of RS & GIS Application Xinjiang Urumqi 830011 China School of Information Science and Engineering Xinjiang University Urumqi 830017 China Department of Computer Vision & Remote Sensing Technische Universität Berlin 10587 Berlin Germany

Evapotranspiration (ET) is a key process in the water cycle. While machine learning-based methods have been increasingly applied to ET estimation, they typically neglect physical mechanisms and ecological impacts. This study proposes a physics-constrained hybrid model (TST-PHY) that incorporates Penman-Monteith-derived physical knowledge into the time series transformer (TST). The framework integrates Bayesian optimization to automatically determine optimal weights for physical mechanisms within the data-driven modeling workflow. The arid and semi-arid regions of Northern China (ASNC) were chosen as the study area, and our results show that the proposed framework optimally balances estimation error and the physical law, with value and trend constraint weights of 0.26 and 0.41, respectively. Moreover, proper physical constraints can improve the prediction accuracy, generalization, and physical consistency of TST-PHY at various spatiotemporal scales. In conclusion, this study establishes a novel paradigm for evapotranspiration estimation in data-sparse regions through the synergistic integration of data-driven and knowledge-based models.

关键词： Evapotranspiration Hybrid model Transformer Physical mechanism Bayesian optimization

A cooperative scheduling framework for shared transportation services 19

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A cooperative scheduling framework for shared transportation...

19th COTA International Conference of Transportation Professionals: Transportation in China - Connecting the World, CICTP 2019

作者： Wang, Xinli Qiao, Yanan Wang, Tianyu Guo, Jifu Zhou, Xuesong Xian, Kai Tong, Lu Carol Xie, Jindong Natl. Eng. Lab. for Compreh. Transp. Big Data Applic. Technol. Sch. of Transp. Sci. and Engineering Beihang Univ. Beijing100191 China State Key Lab of Software Development Environment School of Computer Science and Engineering Beihang Univ. Beijing100191 China Beijing Transport Institute No. 9 LiuLiQiao South Lane Fengtai District China

ISBN: (数字)9780784482292

ISBN: (纸本)9780784482292

To improve the efficiency of metropolitan shared transportation services (STS), this paper proposes a cooperative scheduling framework to avoid congestion. Two procedures are considered: shared path scheduling and congestion solving. In a known static traffic network, classic vehicle routing problem (VRP) solutions may obtain an optimal path. But with an increasing number of shared transportation services required, congestion is likely, triggered by open loop VRP scheduling, especially when shared vehicles overwhelm private ones. Dynamic traffic assignment (DTA) solutions could be employed for short-term traffic situation prediction. However, VRP and DTA cannot be combined linearly, due to uncontrolled recursive calls of routing procedures within VRP and DTA. Hence, a software structure is further studied to guarantee convergence of the cooperative framework. Using two open-source transportation packages, VRPLite and DTALite, we test our framework with the Western 3rd Ring Road of the Beijing regional network. Results verify the framework's usability and flexibility. © ASCE.

关键词： Scheduling

Personalized group of readers' emotion traits modeling based on sentimental analysis of novels

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Personalized group of readers' emotion traits modeling based...

IEEE International Conference on Cognitive Informatics

作者： Yuya Hatakeyama Runhe Huang Bowen Du Neil Yen Haiquan Wang Graduate School of C.I.S Hosei University Japan State Key Lab of Software Development Environment Beihang University China School of Computer Science and Engineering University of Aizu Japan College of Software Beihang University China

ISBN: (数字)9781728114194

ISBN: (纸本)9781728104966

Studies concerning sentiment analysis haven drawn attentions from a wide spectrum of researchers in the past few years. One of the most eye-catching topics within this emerging field is how emotion can be dectected and extracted properly from the given contexts. This study chooses content from novel to stimulate above mentioned long contexts, and expects analysis results can be projected to a three-dimensions space model. Two major tasks are conducted. First, specific content selected from novel is proceeded through morpheme analysis to estimate emotion status. Second, obtained emotion status is calculated through existing emotion dictionary which was built based on data from the novel. Results are then demonstrated on the three-dimensions space model. At last, two experiments, a systematic way to compare emotion status and an empirical questionnaire, are conducted to verify the performance of proposed method.

关键词： Dictionaries Analytical models Estimation Mathematical model Computational modeling software Sentiment analysis

Local and global structure-aware entropy regularized mean teacher model for 3d left atrium segmentation 23rd

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Local and global structure-aware entropy regularized mean te...

23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Hang, Wenlong Feng, Wei Liang, Shuang Yu, Lequan Wang, Qiong Choi, Kup-Sze Qin, Jing School of Computer Science and Technology Nanjing Tech University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Department of Radiation Oncology Stanford University StanfordCA94305 United States CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China School of Nursing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783030597092

Emerging self-ensembling methods have achieved promising semi-supervised segmentation performances on medical images through forcing consistent predictions of unannotated data under different perturbations. However, the consistency only penalizes on independent pixel-level predictions, making structure-level information of predictions not exploited in the learning procedure. In view of this, we propose a novel structure-aware entropy regularized mean teacher model to address the above limitation. Specifically, we firstly introduce the entropy minimization principle to the student network, thereby adjusting itself to produce high-confident predictions of unannotated images. Based on this, we design a local structural consistency loss to encourage the consistency of inter-voxel similarities within the same local region of predictions from teacher and student networks. To further capture local structural dependencies, we enforce the global structural consistency by matching the weighted self-information maps between two networks. In this way, our model can minimize the prediction uncertainty of unannotated images, and more importantly that it can capture local and global structural information and their complementarity. We evaluate the proposed method on a publicly available 3D left atrium MR image dataset. Experimental results demonstrate that our method achieves outstanding segmentation performances than the state-of-the-art approaches in scenes with limited annotated images. © Springer Nature Switzerland AG 2020.

关键词： Forecasting