Invited Speakers

Prof. Wenbing Tao
Huazhong University of Science and Technology, China

Wenbing Tao is a professor and doctoral supervisor at the School of Artificial Intelligence and Automation (AIA), Huazhong University of Science and Technology (HUST). He also works at the National Key Laboratory of Multi-spectral Intelligent Information Processing Technology. Currently, he servers as the first executive director of the Hangzhou Artificial Intelligence Research Institute (in preparation), which is jointly established by HUST and Linping District of Hangzhou municipal government. He has been selected as one of the Highly Cited Chinese Researchers by Elsevier for six consecutive years. He has published more than 100 papers as the first author or corresponding author in journals and conferences such as TPAMI, IJCV, TIP, CVPR, NeurIPS, AAAI, etc., and has been granted more than 50 invention patents. In addition, many research results have been successfully transformed and applied in enterprises, generating significant economic value.
His research work in recent years has mainly focused on the following aspects: 1) Proposed a series of multi-view reconstruction algorithms to achieve leading performance on public data sets, which are widely adopted by the industry (open source code); 2) Proposed several depth The learning surface reconstruction algorithm solves the problem of large-scale point cloud surface reconstruction and achieves performance comparable to traditional geometric algorithms (open source code); 3) Several performance SOTA algorithms have been proposed in the field of point cloud registration (open source code).



Prof. Linlin Shen
Shenzhen University, China

Professor Linlin Shen received Ph.D. degree from the University of Nottingham, Nottingham, UK. Prof. Shen is currently the “Pengcheng Scholar” Distinguished Professor at School of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China. He is also an Honorary professor at School of Computer Science, University of Nottingham, UK and Visiting Professor at School of Computer Science, University of Nottingham, Ningbo, China. He serves as the director of Computer Vision Institute, AI Research Center for Medical Image Analysis and Diagnosis and China-UK joint research lab for visual information processing. He also serves as the Co-Editor-in-Chief of the IET journal of Cognitive Computation and Systems.
He is listed as the “Most Cited Chinese Researchers” by Elsevier, and listed in a ranking of the “Top 2% Scientists in the World” by Stanford University. He received the Most Cited Paper Award from the journal of Image and Vision Computing. His cell classification algorithms were the winners of the International Contest on Pattern Recognition Techniques for Indirect Immunofluorescence Images held by ICIP 2013 and ICPR 2016.



Assoc. Prof. Ioannis Ivrissimtzis
Durham University, UK

Ioannis Ivrissimtzis received a BSc in Mathematics from the Aristotle University of Thessaloniki, Greece, and a PhD in Mathematics from the University of Southampton, UK. Currently, he is an Associate Professor at the Department of Computer Science at Durham University, UK. His research, which is documented in about 100 peer-reviewed papers in journals and conference proceedings, has contributed to the areas of geometric group theory, computer graphics and geometric modelling. In particular, he has studied regular surface tessellations, subdivision surfaces, triangle mesh compression, surface reconstruction, and triangle mesh watermarking and steganalysis. His most recent work is on applied machine learning and computer vision applications. In particular, he has worked on presentation attack detection on face recognition systems, bias in presentation attack detection, watermark retrieval from 3D printed objects, and personalisation in sign language recognition systems.



Prof. Xin Xu
Wuhan University of Science and Technology, China

Xin Xu received the B.S. and Ph.D. degrees in computer science and engineering from Shanghai Jiao Tong University, Shanghai, China, in 2004 and 2012 respectively. He is currently a Full Professor with the School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China. His research deals with image processing, computer vision, and deep learning. More specifically, his research areas focus on building a hierarchical person re-identification architecture including detection and recognition for nighttime surveillance scenarios. His publication was selected as the cover paper of the journal International Journal of Intelligent Systems in 2022. He was shortlist in the Best Paper Finalist of the IEEE International Conference on Multimedia and Expo (ICME) 2021.



Assoc. Prof. Binjie Qin
Shanghai Jiao Tong University, China

Binjie Qin received his M.Sc. and Ph.D. degrees from the Nanjing University of Science and Technology, Nanjing, and Shanghai Jiao Tong University, Shanghai, China, in 1999 and 2002, respectively. From 2012 to 2013, He was a Visiting Professor with the Department of Computer Science, University College London, U.K. He is currently an Associate Professor with the School of Biomedical Engineering, Shanghai Jiao Tong University. His current research interests include biomedical imaging, image processing, machine learning, computer vision, and biomedical instrumentation. He has served as an associate editor of Frontiers in Neuroscience, a reviewer for more than thirty journals including IEEE Transactions on Image Processing, IEEE Transactions on Neural Networks and Learning Systems, Medical Image Analysis, Pattern Recognition. He is a leading guest editor of “Cerebral vessel extraction: from image acquisition to machine learning” in Frontiers in Neuroscience 2021-2022. He was awarded as an outstanding reviewer of IEEE Transactions on Instrumentation and Measurement 2022, outstanding reviewer of Pattern Recognition 2018 and Journal of Shanghai Jiaotong University 2020-2022. His group has developed several imaging systems for image guided ENT surgery and small animal fluorescence imaging, which have preclinical and clinical applications in some laboratories and hospitals. Prof. Qin and his group have published more than 60 papers with over 30 papers in SCI/EI indexed journals including IEEE Transactions Medical Imaging, IEEE Transactions on Image Processing, Pattern Recognition. Over the past years, his group’s research has focused on several challenging problems such as heterogeneous vessel extraction from X-ray coronary angiography in minimally invasive vascular interventions.



Prof. Jiaqiu Ai
Hefei University of Technology, China

Jiaqiu Ai received his BS degree in Electronics & Information from the Beijing Information Science and Technology University in 2007, and his PhD degree in Information & Communication from University of Chinese Academy of Sciences in 2012. He is currently a professor at Hefei University of Technology (HFUT), Hefei, China. He is the author of more than 50 journal papers. He is the editorial member of Journal of Remote Sensing, Computer Engineering, and Current Chinese Sciences. He is one of the Hefei Leading Talents, and a Young Academic Talent of HFUT. He is the best reviewer for Journal of Remote Sensing, and Journal of Radar. His current research interests include SAR image processing, radar target detection and radar system design.

Speech Title: Research on Finer Sea-land Segmentation Method Based on Space-borne SAR/Optical/AIS Multi-modal Data

This research comprehensively considers the accuracy and timeliness of sea-land segmentation, medium-resolution wide-swath space-borne SAR imagery, optical remote sensing data, and AIS data are used as the sources for large-scale finer segmentation to improve the accuracy of sea-land segmentation methods. In response to the problems faced by traditional sea-land segmentation methods in complex environment, of which are " boundary pixel blurring", "incomplete single-source feature representation ", and "insufficient segmentation accuracy", this study fully exploits the spatio-temporal correlation of sea-land features in multi-modal data such as space-borne SAR imagery, optical data, and AIS data. It constructs a robust new feature, the spatio-temporal correlation of multi-modal data, and integrates this feature into the whole process of sea-land segmentation, guiding the clear delineation of boundaries. The spatio-temporal correlation model of multi-modal data is constructed, which can deeply integrate the complementary information of cross-temporal and cross-spatial correlations between sea and land features in space-borne SAR imagery, optical data, and AIS data. This facilitates a comprehensive representation of the spatio-temporal relationships between sea and land features. A sea-land segmentation framework based on multi-modal data spatio-temporal association learning is built, and a high-precision classifier that takes into account both temporal and spatial features is designed to improve the accuracy of finer sea-land segmentation. The research results can solve the contradiction between accuracy and timeliness in the field of sea-land segmentation, and promote the application of multi-source data in maritime monitoring and management.



Assoc. Prof. Yan Pang
Guangzhou University, China

Yan Pang, Ph.D., serves as an Associate Professor at Guangzhou University after earning his doctoral degree from the University of Colorado, USA. Prior to his present position, he was an instructor at the Metropolitan State University of Denver and the University of Colorado Denver. He also gained industry experience as a Senior Machine Learning Engineer at Moffett AI, a well-known Silicon Valley company. His primary research revolves around computer vision, where he conducts systematic theoretical research and practical applications, particularly in medical image analysis, behavior recognition and analysis, graph neural networks, multimodal learning, blockchain, and remote sensing image analysis, et al. Dr. Pang has published more than 10 papers in SCI/SSCI indexed journals including IEEE Transactions Medical Imaging, IEEE Transactions on Neural Networks and Learning Systems, His significant contributions have been applied practically in diverse sectors such as medicine, agriculture, and security, making a substantial impact in their intelligent evolution.



Dr. Yubing Tong
University of Pennsylvania, USA

Yubing Tong, PhD,  is the Director of Operations for the Medical Image Processing Group (MIPG) and a Senior Research Investigator at the University of Pennsylvania. He has successfully developed a 4D dynamic MR image construction approach and a dynamic MRI based patient treatment evaluation approach (in collaboration with Dr. Jay Udupa, Chief of MIPG) that both have been applied in an ongoing thoracic insufficiency syndrome (TIS) collaborative research project at the Children’s Hospital of Philadelphia (CHOP). His research contributions on dynamic MRI technique for studying TIS has been lauded with best paper awards (as the 1st author) by the Scoliosis Research Society (SRS 2020) and the International Congress on Early Onset Scoliosis (ICEOS 2020). In addition, he has been participating in many ongoing clinical translational research projects related to medical oncology, radiation oncology using CT and PETCT, pulmonary medicine, cardiovascular medicine, radiology, and other clinical domains that utilize advanced artificial intelligence technologies. His research is majorly supported by NIH (R01, R21) and NSF funds. He led two deep learning projects with the support from ITMAT at the University of Pennsylvania (2018-2021). He helps to build automatic anatomy recognition (AAR) software for radiation therapy application (AAR-RT)  which was approved (with 510(k) clearance from FDA), 2021. Dr. Tong has around 150 published peer-reviewed journal and conference papers with 3 granted patents, and has served as a reviewer for multiple international journals (>20) and conferences (>10) and has also served as a grant reviewer for the NSF. Currently, he is a senior IEEE member.



Prof. Jingjing Si
Yanshan University, China

Jingjing Si received the B.S. degree in Electronics Engineering and the M.S. degree in Communication and Information System from Yanshan University, in 2002 and 2005, respectively. She received the Ph.D. in Communication and Information System from Beijing University of Posts and Telecommunications in 2010. She is currently a professor with the School of Information Science and Engineering, Yanshan University. Her research interests include multi-media signal processing, computer vision, and optimization. She has presided over 2 National Natural Science Foundation of China, and 8 provincial and ministerial level research projects. She has published more than 60 high-level papers in academic journals and academic conferences.



Assoc. Prof. Lei Chen
Shandong University, China

Lei Chen received the B.Sc. and M.Sc. degrees in electrical engineering from Shandong University, Jinan, China, and the Ph.D. degree in electrical and computer engineering from University of Ottawa, Ontario, Canada. He is currently an Associate Professor with the School of Information Science and Engineering, Shandong University, China. His research interests include signal processing and computer vision, visual quality assessment and pattern recognition, machine learning and artificial intelligence. He was the principal investigator of projects granted from the National Natural Science Foundation of China, National Natural Science Foundation of Shandong Province, China Postdoctoral Science Foundation, etc. He has published more than 40 papers on top international journals and conferences in recent years including IEEE TIP, Signal Process., ICME, etc. He was awarded the Future Plan for Young Scholars of Shandong University. He served for many international conferences including the ICIGP 2021, IoTCIT 2022, MLCCIM 2022, ICCEE 2023 as Program Chair, Technical Chair or Publicity Chair.



Prof. Tiejun Yang
Guilin Medical University, China

Tiejun Yang is a male professor and master's supervisor, born in June 1979, currently employed at the School of Intelligent Medicine and Biotechnology, Guilin Medical University. He holds a Ph.D. and his research focuses on deep learning, computer vision, and bioinformatics. He has extensive experience in applied research in computer vision and artificial intelligence technology. Tiejun Yang pursued his Bachelor's degree in Computer Science and Technology and Master's degree in Software and Theory at Central South University from 1998 to 2005. He then went on to pursue a Ph.D. in Computer Application Technology at South China University of Technology from 2005 to 2008, successfully obtaining his Ph.D. degree in June 2008.
Throughout his career, Tiejun Yang has undertaken two projects funded by the National Natural Science Foundation and three projects funded by the Guangxi Natural Science Foundation. He has authored over 20 papers published in reputable journals such as "Journal of Electronics (China)" and "Journal of Intelligent Manufacturing," including 10 SCI papers. In addition, he has been granted four invention patents and eight software copyrights. Tiejun Yang's contributions have been recognized with the third prize in the Guangxi Science and Technology Progress Award, specifically in the category of scientific and technological progress.



Assoc. Prof. Tong Qiao
Hangzhou Dianzi University, China

Tong Qiao received the B.S. degree in Electronic and Information Engineering in 2009 from Information Engineering University, Zhengzhou, China, and the M.S. degree in Communication and Information System in 2012 from Shanghai University, Shanghai, China, and the Ph.D. degree in University of Technology of Troyes, Laboratory of Systems Modelling and Dependability, Troyes, France, in 2016. He currently works as an Associate Professor in School of Cyberspace from Hangzhou Dianzi University. His current research interests focus on media forensics, AI security and data hiding. He has published over 60 peer-reviewed papers on journals and conferences, including IEEE TPAMI, IEEE TIFS, IEEE TDSC, IEEE TMM, IEEE TCDS. He is currently the head of Sino-France Joint Laboratory for Digital Media Forensics of Zhejiang Province, the member of the Technical Committee on Digital Media Forensics and Security of CSIG, the member of the Technical Committee on Big Data and Privacy Computing of CIPS, and an associate editor of IET Image Processing.



Prof. Indrarini Dyah Irawati
Telkom University, Indonesia

Indrarini Dyah Irawati, obtained B.E and M.E degree in Electrical Engineering at Telkom University, Bandung, Indonesia and doctoral degree in the School of Electrical and Information Engineering, Institute of Technology Bandung. She joined School of Applied Science, Telkom University as an Instructor (2007-2013), Assistant Professor (2014-2018), Associate Professor (2019-2023), Professor (2023). Her main research interests are in the areas of compressive sensing, watermarking, signal processing, computer network, and IoT.
She is currently a member of the Association for Computing Machinery (ACM), the International Association of Engineers (IAENG), and IEEE. She has published more than 50 research papers-mostly the top ranked journal Scopus Indexed by IEEE, Springer, Elsevier, and 20 Conference papers-mostly the top ranked international conference, 6 books, 3 book chapter, 32 Intellectual property rights, and 1 patent. She has been session chair in number of International and National conferences and also guest editor in reputed International journals and reviewer of IEEE Access, International Journal on Electrical Engineering and Informatics (IJEEI), KSII Transactions on Internet and Information Systems, Evolutionary Intelligence, International Conference on Frontiers of Systems, Process and Control (FSPC), International Conference on Electronics, Computer, and Communication Engineering (ICECC), International Conference on Information and Communication Technology (ICOICT), International Conference on Internet of Things and Intelligence System (IoTaIS).



Assoc. Prof. Dakshina Ranjan Kisku
National Institute of Technology Durgapur, India

Dakshina Ranjan Kisku is currently an Associate Professor in the Department of Computer Science and Engineering at National Institute of Technology Durgapur, India. He received his BCSE, MCSE and Ph.D. (Engineering) degrees in Computer Science and Engineering from Jadavpur University, India. Dr. Kisku is an active researcher in the area of biometrics, image processing, computer vision, machine learning and deep learning. He was a Researcher in the Computer Vision Lab at the University of Sassari, Italy from March 2006 to March 2007. Prior to that, he was a Project Associate in Biometrics and Computer Vision Lab at Indian Institute of Technology Kanpur, India. He was a Postdoctoral Scientist in the Signal Processing Laboratory at Bahcesehir University, Turkey in 2012. Prior to joining NIT Durgapur, he was working as a faculty member for several years at Asansol Engineering College, India and Dr. B. C. Roy Engineering College, India. Dr. Kisku has more than 130 scientific publications to his credit published in refereed conferences, journals and edited books. He has co-authored and edited three books on biometrics and homeland security, and one book on medical biometrics. He has published three patents in the area of Healthcare Informatics for publication. He is a recipient of IEI Young Engineers Award, IEI Excellence Award, Outstanding Scientist Award, Outstanding Reviewers Awards, IEEE Travel Award, IAPR Endorsement Scholarship, MIUR Research Fellowship, TUBITAK PDF Fellowship, Visvesvaraya Fellowship and many other accolades. Dr. Kisku is a Fellow of RSPH, UK and Senior Member of IEEE (USA). He regularly serves as a member of technical committee for many conferences and also reviewer for many refereed journals, conferences and books.

Speech Title: Faster and Precise Image Captioning

Abstract: We propose an efficient image captioning model which makes use of entity relations and a deep learning-based encoder and decoder model. In order to make image captioning precise, the proposed model uses Inception-Resnet(version-2) as an encoder and GRU as a decoder. To make the model less expensive and effective, the training process is accelerated by reducing the effect of vanishing gradient issues with residual connections introduced in Inception architecture. Furthermore, the effectiveness of the proposed model has been significantly enhanced by associating the Bahadanu Attention model with GRU. To cut down the computation time and make it a less resource-consuming captioning model, a compact form of the vocabulary of informative words is taken into consideration. The proposed work makes use of the convolution base of the hybrid model to start learning alignment from scratch and learn the correlation among different images and descriptions. The proposed image text generation model is evaluated on Flickr 8k, Flickr 30k, and MSCOCO datasets, and it produces convincing results on assessments.