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14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2024

作者： Wan, Huang Xie, Shaoxiong Luo, Tian Ma, Siqi Zhao, Lin Li, Wen Hu, Wenjing Hunan Institute of Science and Technology School of Information Science and Engineering Yueyang414006 China Hunan Institute of Science and Technology Hunan Engineering Technology Research Center for 3D Reconstruction and Intelligent Application Yueyang China

ISBN: (纸本)9798331513139

Hyperspectral object tracking (HOT) is a crucial research area in computer vision and remote sensing. Unlike RGB-based trackers, HOT can more accurately capture the intrinsic material characteristics of objects. However, the limited number of training samples in hyperspectral datasets presents challenges for developing robust hyperspectral trackers. To tackle this issue, prompt learning is utilized to enhance spectral information in the input space while leveraging prior knowledge from foundational trackers. In this paper, we introduce a novel Spectral Prompt Adaptation Tracker (SPA-Tracker) that learns valuable prompts from hyperspectral images and adapts prompt information with hyperspectral, even when labeled training data is scarce. Compared to the baseline, SPA-Tracker includes two distinct modules: 1) A Spectral Information Prompt (SIP) module that extracts useful spectral information to enrich the input;and 2) An efficient Embedded Adapter (EA) module that facilitates learning interactions between RGB and hyperspectral features, thereby minimizing reliance on pre-trained RGB trackers. Extensive experiments demonstrate that our method achieves outstanding performance on HOT tasks. © 2024 IEEE.

关键词： Object tracking

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VP-HOT: Visual Prompt for Hyperspectral Object Tracking 13

VP-HOT: Visual Prompt for Hyperspectral Object Tracking

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13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2023

作者： Xie, Shaoxiong Li, Jia Zhao, Lin Hu, WenJing Zhang, Guoyun Wu, Jianhui Li, Xinpin Hunan Institute of Science and Technology School of Information Science and Engineering Yueyang414006 China Hunan Institute of Science and Technology Hunan Engineering Technology Research Center for 3D Reconstruction and Intelligent Application Yueyang China

ISBN: (纸本)9798350395570

Hyperspectral object tracking (HOT) has received increasing attention due to the property of hyperspectral Image (HSI) underlying material information of background and foreground, and it shows the potential to outperform conventional RGB-based tracking methods, especially in complex scenarios. However, it is difficult to train a powerful hyperspectral object tracker due to the lack of large-scale HSI-based datasets. To address this problem, we develop visual prompt hyperspectral object tracking (VP-HOT). In order to leverage the knowledge of the fundamental RGB model, our methodology involves the freezing of pre-trained model parameters and the incorporation of hyperspectral images (HSIs) as visual prompts, with only 1.7 million trainable parameters. This adaptation improves the suitability of the model for HOT tasks. Extensive experiments demonstrate the potential of visual prompt learning in HOT, and VP-HOT achieves outstanding performance across diverse downstream tasks, spanning VIS (16 bands), RedNIR (15 bands), and NIR (25 bands). © 2023 IEEE.

关键词： Large datasets

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Mvp-Hot: A Moderate Visual Prompt for Hyperspectral Object Tracking

SSRN

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SSRN 2024年

作者： Zhao, Lin Xie, Shaoxiong Li, Jia Tan, Ping Hu, Wenjin Hunan Institute of Science and Technology Yueyang414006 China Hunan Engineering Technology Research Center for 3D Reconstruction and Intelligent Application Yueyang414006 China

The growing attention to hyperspectral object tracking (HOT) can be attributed to the extended spectral information available in hyperspectral images (HSIs), especially in complex scenarios. This potential makes it a promising alternative to traditional RGB-based tracking methods. However, the scarcity of large hyperspectral datasets pose a challenge for training robust hyperspectral trackers using deep learning methods. Prompt learning, a new paradigm emerging in large language models, involves adapting or fine-tuning a pre-trained model for downstream task by providing task-specific inputs. We propose a novel prompt learning method for HOT tasks, termed Moderate Visual Prompt for HOT (MVP-HOT). Specifically, MVP-HOT freezes the parameters of the pre-trained model and employs HSIs as visual prompts to leverage the knowledge of the underlying RGB model. Additionally, we develop a moderate and effective strategy to incrementally adapt the HSI prompt information. Our proposed method uses only a few (1.7M) learnable parameters and demonstrates its effectiveness. © 2024, The Authors. All rights reserved.

关键词： Large datasets

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SPA-Tracker: Spectral Prompt Adaptation for Hyperspectral Object Tracking

SPA-Tracker: Spectral Prompt Adaptation for Hyperspectral Ob...

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Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS

作者： Huang Wan Shaoxiong Xie Tian Luo Siqi Ma Lin Zhao Wen Li Wenjing Hu Hunan Institute of Science and Technology School of Information Science and Engineering Yueyang China Hunan Engineering Technology Research Center for 3D Reconstruction and Intelligent Application Hunan Institute of Science and Technology Yueyang

ISBN: (数字)9798331513139

ISBN: (纸本)9798331513146

Hyperspectral object tracking (HOT) is a crucial research area in computer vision and remote sensing. Unlike RGB-based trackers, HOT can more accurately capture the intrinsic material characteristics of objects. However, the limited number of training samples in hyperspectral datasets presents challenges for developing robust hyperspectral trackers. To tackle this issue, prompt learning is utilized to enhance spectral information in the input space while leveraging prior knowledge from foundational trackers. In this paper, we introduce a novel Spectral Prompt Adaptation Tracker (SPA-Tracker) that learns valuable prompts from hyperspectral images and adapts prompt information with hyperspectral, even when labeled training data is scarce. Compared to the baseline, SPA-Tracker includes two distinct modules: 1) A Spectral Information Prompt (SIP) module that extracts useful spectral information to enrich the input; and 2) An efficient Embedded Adapter (EA) module that facilitates learning interactions between RGB and hyperspectral features, thereby minimizing reliance on pre-trained RGB trackers. Extensive experiments demonstrate that our method achieves outstanding performance on HOT tasks.

关键词： Training Visualization Training data Signal processing Feature extraction data models Object tracking Proposals data mining Hyperspectral imaging

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VP-HOT: Visual Prompt for Hyperspectral Object Tracking

VP-HOT: Visual Prompt for Hyperspectral Object Tracking

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Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS

作者： Shaoxiong Xie Jia Li Lin Zhao WenJing Hu Guoyun Zhang Jianhui Wu Xinpin Li School of Information Science and Engineering Hunan Institute of Science and Technology Yueyang China Hunan Engineering Technology Research Center for 3D Reconstruction and Intelligent Application Hunan Institute of Science and Technology Yueyang

关键词：

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3d printing for tissue/organ regeneration in China

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Bio-design and Manufacturing 2025年第2期8卷 169-242,I0001,I0002页

作者： Chaofan He Jiankang He Chengtie Wu Changshun Ruan Qi Gu Yongqiang Hao Yang Wu Shuo Bai Xiaoxiao Han Liliang Ouyang Jun Yin Hongzhao Zhou Zhuo Xiong Maobin Xie Lei Shao Jing Nie Liang Ma Cijun Shuai Changchun Zhou Xin Zhao Xuetao Shi Mengfei Yu Jiayin Fu Peng Wen Huixia Xuan Yuan Pang Yan’en Wang Yuan Sun Ziqi Gao Abdellah Aazmi Jingbo Zhang Tianhong Qiao Qixiang Yang Ke Yao Mao Mao Jianxin Hao Pinpin Wang Jirong Yang Huawei Qu Xinhuan Wang Xin Liu Shen Ji Shasha Liu Jingke Fu Bingxian Lu Mohan Wu Feng Chen Zihao Zheng Boqing Zhang Muyuan Chai Chaoying Zhang Mouyuan Sun Bo Peng Huayong Yang Yong He State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical EngineeringZhejiang UniversityHangzhou 310027China Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province School of Mechanical EngineeringZhejiang UniversityHangzhou 310027China State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong UniversityXi’an 710049China State Key Laboratory of High Performance Ceramics and Superfine Microstructure Shanghai Institute of CeramicsChinese Academy of SciencesShanghai 200050China Research Center for Human Tissue and Organ Degeneration Institute of Biomedicine and BiotechnologyShenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China University of Chinese Academy of Sciences Beijing 100049China Key Laboratory of Organ Regeneration and Reconstruction State Key Laboratory of Membrane BiologyInstitute of ZoologyChinese Academy of SciencesBeijing 100101China Beijing Institute for Stem Cell and Regenerative Medicine Beijing 100101China Shanghai Engineering Research Center of Innovative Orthopaedic Instruments and Personalized Medicine Clinical and Translational Research Center for 3D Printing TechnologyShanghai 200011China School of Biomedical Engineering Harbin Institute of Technology(Shenzhen)Shenzhen 518055China State Key Laboratory of Biochemical Engineering Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China Key Laboratory of Biopharmaceutical Preparation and Delivery Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China National Engineering Research Centre for High Efficiency Grinding Hunan UniversityChangsha 410082China Biomanufacturing Center Department of Mechanical EngineeringTsinghua UniversityBeijing 100084China State Key Laboratory of Tribology in Advanced Equipment Tsinghua UniversityBeijing 100084China The Fourth Affiliated Hospital of Guangzhou Medical University Guangzhou 511436China School of Biomedical Engineering Guangzhou Medic

As surgical procedures transition from conventional resection to advanced tissue-regeneration technologies,human disease therapy has witnessed a great leap *** particular,three-dimensional(3d)bioprinting stands as a landmark in this setting,by promising the precise integration of biomaterials,cells,and bioactive molecules,thus opening up a novel avenue for tissue/organ *** by the editorial board of Bio-design and Manufacturing,this review brings together a cohort of leading young scientists in China to dissect the core functionalities and evolutionary trajectory of 3d bioprinting,by elucidating the intricate challenges encountered in the manufacturing of transplantable *** further delve into the translational pathway from scientific research to clinical application,emphasizing the imperativeness of establishing a regulatory framework and rigorously enforcing quality-control ***,this review outlines the strategic landscape and innovative achievements of China in this field and provides a comprehensive roadmap for researchers worldwide to propel this field collectively to even greater heights.

关键词： 3d printing Bioprinting Tissue engineering Regenerative medicine

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Insight-HXMT and GECAM-C observations of the brightest-of-all-time GRB 221009A

arXiv

引用

arXiv 2023年

作者： An, Zheng-Hua Antier, S. Bi, Xing-Zi Bu, Qing-Cui Cai, Ce Cao, Xue-Lei Camisasca, Anna-Elisa Chang, Zhi Chen, Gang Chen, Li Chen, Tian-Xiang Chen, Wen Chen, Yi-Bao Chen, Yong Chen, Yu-Peng Coughlin, Michael W. Cui, Wei-Wei dai, Zi-Gao Hussenot-desenonges, T. du, Yan-Qi du, Yuan-Yuan du, Yun-Fei Fan, Cheng-Cheng Frontera, Filippo Gao, He Gao, Min Ge, Ming-Yu Gong, Ke Gu, Yu-dong Guan, Ju Guo, dong-Ya Guo, Zhi-Wei Guidorzi, Cristiano Han, da-Wei He, Jian-Jian He, Jun-Wang Hou, dong-Jie Huang, Yue Huo, Jia Ji, Zhen Jia, Shu-Mei Jiang, Wei-Chun Kann, david Alexander Klotz, A. Kong, Ling-da Lan, Lin Li, An Li, Bing Li, Chao-Yang Li, Cheng-Kui Li, Gang Li, Mao-Shun Li, Ti-Pei Li, Wei Li, Xiao-Bo Li, Xin-Qiao Li, Xu-Fang Li, Yan-Guo Li, Zheng-Wei Liang, Jing Liang, Xiao-Hua Liao, Jin-Yuan Lin, Lin Liu, Cong-Zhan Liu, He-Xin Liu, Hong-Wei Liu, Jia-Cong Liu, Xiao-Jing Liu, Ya-Qing Liu, Yu-Rong Lu, Fang-Jun Lu, Hong Lu, Xue-Feng Luo, Qi Luo, Tao Ma, Bin-Yuan Ma, Fu-Li Ma, Rui-Can Ma, Xiang Maccary, Romain Mao, Ji-Rong Meng, Bin Nie, Jian-Yin Orlandini, Mauro Ou, Ge Peng, Jing-Qiang Peng, Wen-Xi Qiao, Rui Qu, Jin-Lu Ren, Xiao-Qin Shi, Jing-Yan Shi, Qi Song, Li-Ming Song, Xin-Ying Su, Ju Sun, Gong-Xing Sun, Liang Sun, Xi-Lei Tan, Wen-Jun Tan, Ying Tao, Lian Tuo, You-Li Turpin, damien Wang, Jin-Zhou Wang, Chen Wang, Chen-Wei Wang, Hong-Jun Wang, Hui Wang, Jin Wang, Ling-Jun Wang, Peng-Ju Wang, Ping Wang, Wen-Shuai Wang, Xiang-Yu Wang, Xi-Lu Wang, Yu-Sa Wang, Yue Wen, Xiang-Yang Wu, Bo-Bing Wu, Bai-Yang Wu, Hong Xiao, Sheng-Hui Xiao, Shuo Xiao, Yun-Xiang Xie, Sheng-Lun Xiong, Shao-Lin Xiong, Sen-Lin Xu, dong Xu, He Xu, Yan-Jun Xu, Yan-Bing Xu, Ying-Chen Xu, Yu-Peng Xue, Wang-Chen Yang, Sheng Yang, Yan-Ji Yang, Zi-Xu Ye, Wen-Tao Yi, Qi-Bin Yi, Shu-Xu Yin, Qian-Qing You, Yuan Yu, Yun-Wei Yu, Wei Yu, Wen-Hui Zeng, Ming Zhang, Bing Zhang, Bin-Bin Zhang, da-Li Zhang, Fan Zhang, Hong-Mei Zhang, Juan Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences 19B Yuquan Road Beijing100049 China Institut für Astronomie und Astrophysik Kepler Center for Astro and Particle Physics Eberhard Karls Universität Sand 1 Tübingen72076 Germany College of Physics Hebei Key Laboratory of Photophysics Research and Application Hebei Normal University Hebei Shijiazhuang050024 China Department of Physics and Earth Science University of Ferrara Via Saragat 1 Ferrara44122 Italy Department of Astronomy Beijing Normal University Beijing100875 China CAS Key Laboratory for Research in Galaxies and Cosmology Department of Astronomy University of Science and Technology of China Hefei230026 China Department of Astronomy Tsinghua University Beijing100084 China Southwest Jiaotong University Chengdu610092 China INAF Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Piero Gobetti 101 Bologna40129 Italy University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing100049 China College of physics Sciences Technology Hebei University No. 180 Wusi Dong Road Lian Chi District Hebei Baoding071002 China INFN - Sezione di Ferrara Via Saragat 1 Ferrara44122 Italy Physics and Space Science College China West Normal University China Yunnan Observatories Chinese Academy of Sciences Yunnan Province Kunming650011 China Key Laboratory of Space Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China School of Astronomy and Space Science Nanjing University Nanjing210093 China School of Physics and Optoelectronics Xiangtan University Yuhu District Hunan Xiangtan411105 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China Institute of Astrophysics Central China Normal University Wuhan430079 China Nevada Center for Astrophysics Department of Physics and Astronomy University of

GRB 221009A is the brightest gamma-ray burst ever detected since the discovery of this kind of energetic explosions. However, an accurate measurement of the prompt emission properties of this burst is very challenging due to its exceptional brightness. With joint observations of Insight-HXMT and GECAM-C, we made an unprecedentedly accurate measurement of the emission during the first ∼1800 s of GRB 221009A, including its precursor, main emission (ME, which dominates the burst in flux), flaring emission and early afterglow, in the hard X-ray to soft gamma-ray band from ∼ 10 keV to ∼ 6 MeV. Based on the GECAM-C unsaturated data of the ME, we measure a record-breaking isotropic equivalent energy (Eiso) of ∼ 1.5 × 1055 erg, which is about eight times the total rest-mass energy of the Sun. The early afterglow data require a significant jet break between 650 s and 1100 s, most likely at ∼ 950 s from the afterglow starting time TAG, which corresponds to a jet opening angle of ∼ 0.7◦ (ηγn)1/8, where n is the ambient medium density in units of cm−3 and ηγ is the ratio between γ-ray energy and afterglow kinetic energy. The beaming-corrected total γ-ray energy Eγ is ∼ 1.15 × 1051 (ηγn)1/4 erg, which is typical for long GRBs. These results suggest that this GRB may have a special central engine, which could launch and collimate a very narrowly beamed jet with an ordinary energy budget, leading to exceptionally luminous gamma-ray radiation per unit solid angle. Alternatively, more GRBs might have such a narrow and bright beam, which are missed by an unfavorable viewing angle or have been detected without distance measurement. Copyright © 2023, The Authors. All rights reserved.

关键词： Gamma rays

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