Workshops
Workshop 1: Cognitive Engineering and Intelligent Interaction. What to expect for your challenges shortly.
Introduction:
Today, after the popularization of generative AI applications, it is difficult to mention a sector that cannot benefit, from low investment. This was one of the biggest changes we have been experiencing recently: the popularization, at low cost (or even at no cost), of the use of AI applications. Not only end users, but corporations themselves, in the most diverse sectors, have benefited from this new moment.
Currently, the concept of AI - Artificial Intelligence is widely discussed around the world, due to numerous aspects, as well as: ChatGPT, Applied Ethics AI, automation of services, the importance of AI to qualify health and bring solutions to diseases, the importance of AI for the advancement of Smart Cities, the modernization of cybersecurity, among other topics that work with AI, being of paramount importance for global humanity. Concretely, AI is still in its infancy. Its impact is unquestionable, but we still have many challenges ahead, even to make its use fair, ethical, and responsible.
Workshop Chair:
Today, after the popularization of generative AI applications, it is difficult to mention a sector that cannot benefit, from low investment. This was one of the biggest changes we have been experiencing recently: the popularization, at low cost (or even at no cost), of the use of AI applications. Not only end users, but corporations themselves, in the most diverse sectors, have benefited from this new moment.
Currently, the concept of AI - Artificial Intelligence is widely discussed around the world, due to numerous aspects, as well as: ChatGPT, Applied Ethics AI, automation of services, the importance of AI to qualify health and bring solutions to diseases, the importance of AI for the advancement of Smart Cities, the modernization of cybersecurity, among other topics that work with AI, being of paramount importance for global humanity. Concretely, AI is still in its infancy. Its impact is unquestionable, but we still have many challenges ahead, even to make its use fair, ethical, and responsible.
Workshop Chair:
Prof. Gabriel Gomes de Oliveira
University of Campinas (UNICAMP), Brazil
He is currently a Researcher at the State University of Campinas (UNICAMP). Develops research projects related to: Artificial Intelligence (AI), Big Data, Intelligent Information Systems (IIS), Internet of Things (IoT), Intelligent Transportation Systems (ITS), Smart Cities, and Sensors. Reviewer of several Congresses and Journals, such as, (ACM, Elsevier, SAGE, Hindawi, IEEE, IET, Taylor Francis, Springer, and Wiley, among others) of national scope and mainly international, with more than 1200 Revisions, recognized by Publons or with Certificates issued. In addition to numerous publications in Conferences (ACM, IEEE, and Springer), and High Impact Factor Journals. YP Chair IEEE Sensor and Systems Joint Council South Brazil (2022 to date). Finally, Editor of Special Series of Scientific Journals, Editor of Springer Nature, (Smart Innovation, Systems, and Technologies), and IOP Publisher Invited to join as Associate Editor of Journals: IET Circuits, Devices & Systems and IET Wireless Sensor Systems (2022 -2025). And Academic Editor of the Journal PLOS ONE.
Submission Method: Please submit the paper via the Openconf system.
Please note: When you submit your papers in Openconf system, please choose "Workshop 1: Cognitive Engineering and Intelligent Interaction. What to expect for your challenges shortly.
Workshop 2: Effective Multimodal Perception and Interactive Learning.
Introduction:
"The whole is greater than the sum of its parts" is a fascinating phenomenon discovered by cognitive neuroscientists, in the cells of the superior colliculus of the brain. That is, the response to a combined visual, auditory, and somatosensory stimulus is greater than the response to these three stimuli presented alone. In multimodal machine learning, we often introduce additional modalities to improve the performance of existing tasks with uni-modality, such as RGB-D scene recognition, audiovisual speech recognition and RGB-optical flow action recognition. Also, we expect to build reliable and interpretable multimodal learning mechanism, as well as effective interaction system for practical multimodal communication. In the workshop, we will focus on recent remarkable advances in multimodal learning, especially on multimodal perception and interactive learning, for discussing the potential directions in building next-generation multimodal learning system.
Workshop Chair:
"The whole is greater than the sum of its parts" is a fascinating phenomenon discovered by cognitive neuroscientists, in the cells of the superior colliculus of the brain. That is, the response to a combined visual, auditory, and somatosensory stimulus is greater than the response to these three stimuli presented alone. In multimodal machine learning, we often introduce additional modalities to improve the performance of existing tasks with uni-modality, such as RGB-D scene recognition, audiovisual speech recognition and RGB-optical flow action recognition. Also, we expect to build reliable and interpretable multimodal learning mechanism, as well as effective interaction system for practical multimodal communication. In the workshop, we will focus on recent remarkable advances in multimodal learning, especially on multimodal perception and interactive learning, for discussing the potential directions in building next-generation multimodal learning system.
Workshop Chair:
Assoc. Prof. Di Hu
Renmin University of China, China
Di Hu, tenured-track Associate Professor at Gaoling School of Artificial Intelligence, Renmin University of China. His research interests include multimodal perception and learning. He has published more than 30 peer-reviewed top conference and journal papers, including TPAMI, NeurIPS, CVPR, ICCV, ECCV etc. He served as PC/Senior PC members of several top-tier conferences, and co-organized several tutorials on top-tier conferences. Di is the recipient of the Outstanding Doctoral Dissertation Award by the Chinese Association for Artificial Intelligence, also the recipient of ACM XI'AN Doctoral Dissertation Award. He is sponsored by the Young Elite Scientists Sponsorship Program by CAST and Awarded the 2022 WuWenJun AI Excellent Young Scientist.
Invited Speakers:
Assoc. Prof. Chao Ma
Shanghai Jiao Tong University, China
Dr. Chao Ma is an associate professor and PhD advisor at Shanghai Jiao Tong University. He received a Ph.D. degree from Shanghai Jiao Tong University. He was a joint-training Ph.D. student at the University of California, Merced. He was a post-doctoral researcher at The University of Adelaide from 2016 to 2018. His research interests focus on computer vision and machine learning. He received an excellent doctoral dissertation award from the China Society of Image and Graphics (CSIG). He is supported by Shanghai Pujiang Program and National Science Fund for Excellent Young Scholars. He has ranked among Elsevier's Highly Cited Chinese Scholars since 2020. His work was cited more than 10 thousand times on Google Scholar. His research outputs were applied to the Huawei Da Vinci chip and its autonomous driving platform, and he won the 2021 Excellent Technology Achievement Award of Huawei.
Assoc. Prof. Boyang Li
Nanyang Technological University, Singapore
Boyang "Albert" Li is a Nanyang Associate Professor at the School of Computer Science and Technology, Nanyang Technological University. His research interests lie in computational narrative intelligence, multimodal learning, and machine learning. In 2021, he received the National Research Foundation Fellowship, a prestigious research award of 2.5 million Singapore Dollars. Prior to that, he worked as a senior research scientist at Baidu Research USA and a research scientist at Disney Research Pittsburgh, where he led an independent research group. He received his Ph.D. degree from Georgia Institute of Technology in 2015 and his Bachelor degree from Nanyang Technological University in 2008. He currently serves as a senior action editor for ACL Rolling Review and an associate editor for IEEE Transactions on Audio, Speech and Language Processing. His work has been reported by international media outlets such as the Guardian, New Scientist, US National Public Radio, Engadget, TechCrunch, and so on.
Asst. Prof. Yang liu
Peking University, China
Yang Liu is a Tenure-track Assistant Professor (Ph.D. Supervisor) in Wangxuan Institute of Computer Technology, Peking University. Before joining Peking University, she was a Postdoctoral Researcher in the Visual Geometry Group (VGG) at University of Oxford, supervised by Prof. Andrew Zisserman. She received PhD and MPhil in Advanced Computer Science from University of Cambridge, and B.Eng. in Telecommunication Engineering from Beijing University of Posts and Telecommunications (BUPT). Her research interests include computer vision, natural language processing and multi-modal machine learning, with an emphasis on how these areas can collaborate best to perform real-world tasks.
Asst. Prof. Junwei Liang
The Hong Kong University of Science and Technology(Guangzhou campus), China
Dr. Junwei Liang is a tenure-track Assistant Professor at The Hong Kong University of Science and Technology (Guangzhou). He is also affiliated with HKUST CSE. He was a senior researcher at Tencent Youtu Lab working on cutting-edge computer vision research and applications. Before that, he received his Ph.D. from Carnegie Mellon University, working with Prof. Alexander Hauptmann. He is the recipient of the Baidu Scholarship, Yahoo Fellowship and ICCV Doctoral Consortium Award. He received the Rising Star Award at the World AI Conference in 2020. He is the winner of several public safety video analysis competitions, including NIST ASAPS and TRECVID ActEV. His work has helped and been reported by major news agencies like the Washington Post and New York Times. His research interests include human trajectory forecasting, action recognition, and large-scale computer vision. His mission: develop AI technologies for social good.
Submission Method: Please submit the paper via the Openconf system.
Please note: When you submit your papers in Openconf system, please choose "Workshop 2: Effective Multimodal Perception and Interactive Learning