Plenary Speakers



Rama Chellappa
Professor, Johns Hopkins University
Title of the Talk: Maya for AI and AI for Maya: The Promises and Perils of Generative Image and Video Models


Abstract of the Talk: Over the last decade, algorithms and systems based on generative adversarial networks, transformers, diffusion models and neural radiance fields have revolutionized the field of artificial intelligence. While sensors capture what has occurred, the generative models can visualize “what if” scenarios, which help in generalization, learning without annotations and privacy concerns. However, the generative models can also be misused to create fake images and videos. In this talk, I will discuss the progress made in building generative image and video models with applications in computer vision, graphics, medicine, and art, as well as the challenges posed by them.


About the Speaker: Prof. Rama Chellappa is a Bloomberg Distinguished Professor in the Departments of Electrical and Computer Engineering and Biomedical Engineering at Johns Hopkins University (JHU). At JHU, he is also affiliated with CIS and CLSP, Malone Center and MINDS. He holds a non-tenure position as a College Park Professor in the ECE department at UMD. His current research interests span many areas in artificial intelligence, computer vision, and machine learning. Prof. Chellappa is an elected member of the National Academy of Engineering. He received the 2020 Jack S. Kilby Medal for Signal Processing from the IEEE and the K.S. Fu Prize from the International Association of Pattern Recognition (IAPR). He is a recipient of the Society, Technical Achievement and Meritorious Service Awards from the IEEE Signal Processing Society. He also received the Technical Achievement and Meritorious Service Awards from the IEEE Computer Society. Recently, he received the inaugural Leadership Award from the IEEE Biometrics Council. At UMD, he has received numerous college and university level recognitions for research, teaching, innovation and mentoring of undergraduate students. In 2010, he was recognized asan Outstanding ECE by Purdue University. He received the Distinguished Alumni Award from the Indian Institute of Science in 2016. He is a Fellow of AAAI, AAAS, ACM, AIMBE, IAPR, IEEE, NAI and OSA and holds eight patents.

C. V. Jawahar
Professor, IIIT Hyderabad
Prof. C. V. Jawahar is the Dean of Research & Development and Head of the Centre for Visual Information Technology-CVIT, and Machine Learning Lab at the International Institute of Information Technology, Hyderabad (IIITH), India. He leads the research group focusing on computer vision, machine learning, and multimedia systems. He is also the CEO of the Centre for Innovation and Entrepreneurship (CIE-IIIT) at IIIT Hyderabad. Prof. Jawahar was awarded a doctorate from IIT Kharagpur in 1997 and has been associated with IIIT Hyderabad since December 2000. He plays a pivotal role in guiding a large group of Ph.D. and MS students on computer vision, machine learning, and multimedia systems. In recent years, he has been actively involved in research questions that converge mobility, health, vision, language, and text. He is also interested in large-scale multimedia systems with a special focus on retrieval. An Amazon Chair Professor, Prof. Jawahar is also an elected Fellow of the Indian National Academy of Engineers (INAE) and the International Association of Pattern Recognition (IAPR). His prolific research is globally recognized in the Artificial Intelligence and Computer Vision research community with more than 100 publications in top tier conferences and journals in computer vision, robotics and document image processing to his credit with over 12000 citations. He is awarded the ACM India Outstanding Contribution to Computing Education (OCCE) 2021 for fundamental, selfless service in teaching of computing, and nurturing a generation of students who now serve the larger society and have led to an impact in multiple dimensions of computing education. He has served as the co-organizer and program chair in top-tier conferences that include ACCV, ECCV, ICCV, ICDAR, ICVGIP, ICFHR and WACV. Presently, he is an area editor of CVIU and an associate editor of IEEE, PAMI and IJDAR. Extremely conscious of the social and practical relevance and application of research, Prof. Jawahar is actively engaged with several government agencies, ministries, and leading companies around innovating at scale through research.

R. Venkatesh Babu
Professor, Indian Institute of Science Bangalore
Title of the Talk: Enhancing Adversarial Robustness in Deep Neural Networks: Recent Advances and Efficient Training Techniques


Abstract of the Talk: Deep learning has achieved unprecedented success in many of the computer vision applications. The data-driven deep models have shown remarkable progress in many ill-posed problems such as 3D reconstruction from 2D images, depth estimation from monocular image, etc. On the other hand, these sophisticated overparameterized deep networks are easily fooled by carefully crafted, imperceptible noise, called adversarial perturbations, added to the image. As more and more sensitive applications were developed based on deep models, adversarial robustness research has gained more attention in recent years. This talk will introduce and explore some recent works towards efficient training of adversarially robust deep models.


About the Speaker: R. Venkatesh Babu is a professor at the Department of Computational and Data Sciences (CDS), Indian Institute of Science (IISc), Bangalore. He received his doctoral degree from the Dept. of Electrical Engineering, Indian Institute of Science, Bangalore. He held postdoctoral positions at NTNU, Norway and IRISA/INRIA, Rennes, France, through an ERCIM fellowship. He also worked as a research fellow at NTU, Singapore. He is the convener of Vision and AI Lab (VAL) (formerly, Video Analytics Lab) at IISc. His research interests include Computer Vision and Machine Learning. He is a recipient of the SERB Star (2020) and Sathish Dhawan Young Engineer awards (2019). He serves as a Program Chair of the AIML-Systems 2023 conference. He is an associate editor for Pattern Recognition and CVIU journals and serves as an area chair for the following conferences: CVPR, AAAI, NeurIPS, ICCV, WACV, ACML, and AISTATS.