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HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model

IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年

作者： Wang, Di Hu, Meiqi Jin, Yao Miao, Yuchun Yang, Jiaqi Xu, Yichu Qin, Xiaolei Ma, Jiaqi Sun, Lingyu Li, Chenxing Fu, Chuan Chen, Hongruixuan Han, Chengxi Yokoya, Naoto Zhang, Jing Xu, Minqiang Liu, Lin Zhang, Lefei Wu, Chen Du, Bo Tao, Dacheng Zhang, Liangpei Wuhan University School of Computer Science China Wuhan University National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering China Zhongguancun Academy China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing China Sun Yat-sen University School of Geography and Planning China Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Chongqing University College of Computer Science China The University of Tokyo Japan RIKEN Center for Advanced Intelligence Project Japan Intelligent Science & Technology Academy Limited CASIC China iFlytek Company Ltd. National Engineering Research Center of Speech and Language Information Processing China Nanyang Technological University College of Computing & Data Science Singapore Henan Academy of Sciences Aerospace Information Research Institute China

Accurate hyperspectral image (HSI) interpretation is critical for providing valuable insights into various earth observation-related applications such as urban planning, precision agriculture, and environmental monitoring. However, existing HSI processing methods are predominantly task-specific and scene-dependent, which severely limits their ability to transfer knowledge across tasks and scenes, thereby reducing the practicality in real-world applications. To address these challenges, we present HyperSIGMA, a vision transformer-based foundation model that unifies HSI interpretation across tasks and scenes, scalable to over one billion parameters. To overcome the spectral and spatial redundancy inherent in HSIs, we introduce a novel sparse sampling attention (SSA) mechanism, which effectively promotes the learning of diverse contextual features and serves as the basic block of HyperSIGMA. HyperSIGMA integrates spatial and spectral features using a specially designed spectral enhancement module. In addition, we construct a large-scale hyperspectral dataset, HyperGlobal-450 K, for pre-training, which contains about 450 K hyperspectral images, significantly surpassing existing datasets in scale. Extensive experiments on various high-level and low-level HSI tasks demonstrate HyperSIGMA's versatility and superior representational capability compared to current state-of-the-art methods. Moreover, HyperSIGMA shows significant advantages in scalability, robustness, cross-modal transferring capability, real-world applicability, and computational efficiency. The code and models will be released at HyperSIGMA. © 1979-2012 IEEE.

关键词： Hyperspectral imaging

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Randomized Latent Factor Model for High-dimensional and Sparse Matrices from Industrial applications

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 131-141页

作者： Mingsheng Shang Xin Luo Zhigang Liu Jia Chen Ye Yuan MengChu Zhou the Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of Sciences the School of Computer Science and Engineering Beihang University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

Latent factor(LF) models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS) matrices which are commonly seen in various industrial applications. An LF model usually adopts iterative optimizers,which may consume many iterations to achieve a local optima,resulting in considerable time cost. Hence, determining how to accelerate the training process for LF models has become a significant issue. To address this, this work proposes a randomized latent factor(RLF) model. It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices, thereby greatly alleviating computational burden. It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix *** results on three HiDS matrices from industrial applications demonstrate that compared with state-of-the-art LF models, RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data.I provides an important alternative approach to LF analysis of HiDS matrices, which is especially desired for industrial applications demanding highly efficient models.

关键词： Big data high-dimensional and sparse matrix latent factor analysis latent factor model randomized learning

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V 2 CT X MXene-derived ammonium vanadate with robust carbon skeleton for superior rate aqueous zinc-ion batteries

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Chinese Chemical Letters 2025年

作者： Xiaojun Wang Yizhou Zhang Linwei Guo Jianwei Li Peng Wang Lei Yang Zhiming Liu College of Electromechanical Engineering Shandong Engineering Laboratory for Preparation and Application of High-performance Carbon-Materials Qingdao University of Science & Technology Qingdao 266061 China Research Center for Intelligent and Wearable Technology College of Textiles and Clothing Qingdao University Qingdao 266071 China

Layered ammonium vanadate has become a promising cathode material for aqueous zinc ion batteries (ZIBs) due to its small mass and large ionic radius of ammonium ions as well as the consequent large layer spacing and high specific capacity. However, the irreversible de-ammoniation caused by N–H…O bonds damaged would impair cycle life of ZIBs and the strong electrostatic interaction between Zn 2+ and V-O frame could slower the mobility of Zn 2+ . Furthermore, the thermal instability of ammonium vanadate also limits the use of common carbon coating modification method to solve the problem. Herein, V 2 CT X MXene was innovatively selected as a bifunctional source to in-situ derivatized (NH 4 ) 2 V 8 O 20 ·xH 2 O with amorphous carbon-coated (NHVO@C) via one-step hydrothermal method in relatively moderate temperature. The amorphous carbon shell derived from the V 2 CT X MXene as a conductive framework to effectively improve the diffusion kinetics of Zn 2+ and the robust carbon skeleton could alleviate the ammonium dissolution during long-term cycling. As a result, zinc ion batteries using NHVO@C as cathode exhibit superior electrochemical performance. Moreover, the assembled foldable or high loading (10.2 mg/cm 2 ) soft-packed ZIBs further demonstrates its practical application. This study provided new insights into the development of the carbon cladding process for thermally unstable materials in moderate temperatures.

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Demand-Driven Sparse Mobile Crowdsensing With Neighborhood-Aware Reconstruction

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IEEE Internet of Things Journal 2025年

作者： Wang, Yuhao Zhou, Qihang Zhang, Yu Deng, Guoqiang Liang, Lingyu Zhang, Xinglin South China University of Technology School of Computer Science and Engineering Guangzhou China South China University of Technology Information and Network Engineering and Research Center Guangzhou China South China University of Technology Guangzhou510641 China Henan Key Laboratory of Chronic Disease Prevention and Therapy Henan Institute of Interconnected Intelligent Health Management Intelligent Health Management Zhengzhou450045 China

Sparse mobile crowdsensing (SMCS) is a cost-effective paradigm aimed at recruiting workers to complete sensing tasks and inferring the remaining unobserved data, with broad applications in large-scale, fine-grained monitoring services. In SMCS, spatial coverage of the sensing area or global completion accuracy is typically used as the performance metric. However, in many real-world service scenarios (e.g., temperature, humidity, air quality monitoring), users are generally only interested in data from their specific regions and expect the highest possible data accuracy. In such cases, relying solely on coverage or global completion error fails to adequately assess the quality of the platform’s service. To address this and satisfy users’ sensing demands as much as possible while maintaining low sensing costs, we propose the Demand-Driven Framework with Neighborhood-Aware Data Reconstruction (D2-SMCS), which integrates regional population demand calculation, dynamic clustering, and data reconstruction. Unlike existing approaches, we introduce quality of service (QoS) as a performance metric based on regional population demand. First, we quantify the interest level of sensing tasks in different regions by considering factors such as population demand and data fluctuation. Based on this quantification, the dynamic clustering module selects the regions most beneficial for accurate data completion. Finally, to overcome the limitation of traditional matrix completion methods in capturing short-term variations, we propose an innovative Neighborhood-Aware Latent Matrix Completion (NALMC) approach to infer and complete the unobserved regions. Extensive experiments on real-world datasets demonstrate the effectiveness of our framework. © 2014 IEEE.

关键词： Quality of service

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research of vision-based navigation and positioning technology for mobile campus intelligent robot

International Journal of Mechatronics and Applied Mechanics

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International Journal of Mechatronics and Applied Mechanics 2019年第6期2卷 46-52页

作者： Yang, Zhixian Li, Dong Henan intelligent education and Intelligent Technology Application Engineering Technology Research Center Henan Province China

This paper mainly studies the autonomous positioning and navigation technology of intelligent robots. In the campus environment to perform security monitoring tasks, this article uses gps to achieve location tracking. Due to the loss of gps signal due to campus buildings and trees, the monocular vision is further selected as a robot-assisted positioning method, and two positioning methods are combined. Achieve effective location monitoring. On the other hand, the use of monocular visual perception of the road environment to achieve autonomous navigation, mainly to achieve the GPS positioning function, manual road sign positioning function, road area detection function. Therefore, the paper tests the artificial road sign location and road identification respectively. The test results show that the acquisition success rate is 96.5% and the average processing speed is 30.34 frames/s without occlusion interference. © 2019, Cefin Publishing House. All rights reserved.

关键词： intelligent robots

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Interpretable text-to-sql generation with joint optimization 1

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17th International Conference on Web Information Systems and applications, WISA 2020

作者： Zhu, Mingdong Wang, Xianfang Zhang, Yang School of Computer Science and Technology Henan Institute of Technology Xinxiang China Intelligent Industrial Big Data Application Engineering Technology Research Center of Xinxiang Xinxiang China

ISBN: (数字)9783030600297

ISBN: (纸本)9783030600280

The purpose of Text-to-SQL is to obtain the correct answer for a textual question from the database, which can take advantage of advanced database system to provide reliable and efficient response. Existing Text-to-SQL methods generally focus on accuracy by designing complex deep neural network models, and hardly consider interpretability, which is very important for serious applications. To address this, in this paper we propose a novel framework for Interpretable Text-to-SQL Generation (ITSG) with joint optimization, which achieves state-of-the-art accuracy and possesses two-level interpretability at the same time. The framework mainly consists of three layers: a sequence encoder which encodes questions, table headers and significant table contents, an attention-based LSTM layer which generates SQL queries and a reinforcement learning layer which boosts the execution accuracy. Comparing with state-of-the-art methods on benchmark datasets, the experimental results show the effectiveness and interpretability of our ITSG framework. © Springer Nature Switzerland AG 2020.

关键词： Reinforcement learning

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Temporal Transformation Network Based On Scale Sequences for Cloth-Changing Person Re-Identification in Video Datasets 9

Temporal Transformation Network Based On Scale Sequences for...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Zhu, Xiaoke Zhao, Bo Dong, Zhiwei Dong, Lingyun Li, Danyang School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Engineering Research Center of Intelligent Technology and Application China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

ISBN: (纸本)9798350317251

This paper introduces the Temporal Transform Network based on Scale Sequences (TTS) for cloth-changing person re-identification in video datasets. The TTS network is designed to capture multi-scale temporal cues within video sequences. It accomplishes this by initially modeling short-term temporal cues between adjacent frames, followed by capturing long-term relationships between non-consecutive frames. In more detail, short-term temporal cues are modeled through parallel inflated convolutions with different time dilation rates, enabling the representation of pedestrian movement and appearance dynamics. Long-term relationships are effectively captured using a temporal self-attention model, mitigating challenges such as occlusion and noise within the video sequence. The TTS network outperforms existing methods across cloth-changing video ReID datasets such as CCVID. For instance, under general settings, our approach exhibits a 1.1% improvement in top-1 accuracy and a corresponding 1.1% increase in mAP compared to the baseline. In cloth-changing settings, we observe a 0.2% enhancement in top-1 accuracy and a notable 1.3% increase in mAP relative to the baseline. © 2023 IEEE.

关键词： Video recording

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Oriented Object Detection in Aerial Images with Double Midpoint Vectors 9

Oriented Object Detection in Aerial Images with Double Midpo...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Zhu, Xiaoke Hu, Qi Chen, Xiaopan Yuan, Caihong Li, Minghao Henan University School of Computer and Information Engineering Kaifeng China Henan University Henan Engineering Research Center of Intelligent Technology and Application China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China

ISBN: (纸本)9798350317251

In recent years, oriented object detection technology has made significant progress. Most oriented object detectors represent oriented bounding boxes by adding additional angle information to the horizontal bounding box representation, which leads to serious angle periodicity issues. To address these issues, this paper proposes double midpoint vectors (DMvectors) to represent oriented objects. For any oriented bounding box, we align the center point with the origin in the Cartesian coordinate system, and evenly distribute the midpoints of the box edges across the four quadrants. We use the midpoint of the first and second quadrants, as well as the width and height of the box, to represent the oriented bounding box. DMvectors solve the periodic problem of angles by implicitly including angle information in vectors. To demonstrate the effectiveness of our proposed method, we conducted extensive experiments on two common remote sensing datasets (DOTA and HRSC2016), and our method achieved competitive results. © 2023 IEEE.

关键词： Object detection

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Point-Level and Set-Level Deep Representation Learning for Cross-Modal Person Re-identification 8

Point-Level and Set-Level Deep Representation Learning for C...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Hu, Jihui Ye, Pengfei Li, Danyang Dong, Lingyun Chen, Xiaopan Zhu, Xiaoke School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China Henan University Henan Engineering Research Center of Intelligent Technology and Application China

ISBN: (纸本)9781665450515

In practice, significant modality differences usually exist between visible and infrared images, which makes visible-infrared Person Re-Identification (VI-ReID) a challenging research task. Due to the existing influence of pose variation, background changes, and occlusion, there are often outlier samples within the set of images from the same person. These outlier samples can adversely affect the process of learning the cross-modal matching model. The existing VI-ReID methods mainly focus on learning cross-modal feature representation by using image-level discriminant constraints, i.e., the distance between the truly-matching cross-modal images should be smaller than that between wrong-matching cross-modal images. However, most of these methods ignore the adverse influence caused by outliers. To solve the above problems, we proposed a Point-level and Set-level Deep Representation Learning (PSDRL) approach for VI-ReID in this paper. By using the set-level constraint in the process of deep representation learning, the discrepancy between visible and infrared modalities can be decreased, and the adverse effect of outliers can be weakened. By employing the image-level constraint, the discriminability of the obtained deep feature representations can be improved. Extensive experiments are conducted on the publicly available cross-modal Person Re-Identification datasets, including SYSU-MM01 and RegDB. Experimental results demonstrate the effectiveness of the proposed approach. © 2022 IEEE.

关键词： Infrared imaging

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Semi-supervised Segmentation and Quantization Algorithm for Infarct Size Measurement of Rat Heart Sections 8

Semi-supervised Segmentation and Quantization Algorithm for ...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Zhu, Xiaoke Cai, Yulong Chen, Xiaopan Hu, Qi Dong, Lingyun Yuan, Caihong School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University China

ISBN: (纸本)9781665450515

Segmentation of rat heart section images and infarct area quantization are essential components of infarct size measurement in processing data from animal experiments on ischemic heart disease. Previously, the task of measuring the infarct size of rat heart sections was performed manually by experienced researchers, which was very costly and time-consuming. In this paper, we propose a two-stage semi-supervised approach to solve the task of ischemic heart infarct size calculation in images of rat section images. The approach uses a semi-supervised semantic segmentation model and a semi-automated quantization algorithm to separate the differently stained regions of rat heart sections. We demonstrate the results of the approach on a rat heart section dataset, and experiments on this dataset emphasize the effectiveness of our approach compared to other methods. © 2022 IEEE.

关键词： Semantic Segmentation

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