PROGRAM

Program

Half-day Event
We expect a half-day event. Preferred date: 11th June. The estimated breakdown among
invited speakers, presentations, and poster sessions is as follows:
• Opening and closing: 15 minutes
• Invited talks: 6 x 30 minutes
• Contributed work presentations: 6 x 10 minutes
• Poster session: 50 minutes
The total amount of time is 390 minutes (6.5 hours), including coffee and lunch breaks.

Time

Program

09:00 AM – 09:15 AM

09:15  AM – 09:45 AM

09:45 AM – 09:55 AM

Opening

Invited Talk1: TBD

Paper Oral 1: TBD

09:55 AM – 10:40 AM 

Coffee Break

10:40 AM – 11:10 AM 

11:10 AM – 11:20 AM 

11:20 AM – 11:30 AM 

11:30 AM – 11:40 AM 

11:40 AM – 12:10 AM 

Invited Talk 2: TBD

Paper Oral 2: TBD

Paper Oral 3: TBD

Paper Oral 4: TBD

Invited Talk 3: TBD

12:10 PM – 12:50 PM

Lunch Break

12:50 PM – 01:20 PM

01:20 PM – 01:50 PM

01:50 PM – 02:00 PM

02:00 PM – 02:10 PM

02:10 PM – 02:40 PM

02:40 PM – 03:30 PM

Invited Talk 4: TBD

Invited Talk 5: TBD

Paper Oral 5: TBD

Paper Oral 6: TBD

Invited Talk 6: TBD

Poster presentation & Coffee Break

12:50 PM – 01:20 PM

01:20 PM – 01:50 PM

01:50 PM – 02:00 PM

02:00 PM – 02:10 PM

02:10 PM – 02:40 PM

02:40 PM – 03:30 PM

Invited Talk 4: TBD

Invited Talk 5: TBD

Paper Oral 5: TBD

Paper Oral 6: TBD

Invited Talk 6: TBD

Poster presentation & Coffee Break

03:30 PM-03:40 PM

Award Announcement & Closing Remarks

Invited Talks

Elisa Ricci

Associate Professor University of Trento e.ricci@unitn.it Confirmed

Elisa Ricci is an Associate Professor with Department of Information Engineering and Computer Science (DISI) at the University of Trento and the head of the Deep Visual Learning research group at Fondazione Bruno Kessler (FBK). She is the scientific manager of the Joint Laboratory on Vision and Learning between FBK and DISI. Her research interests are directed to the development of deep learning algorithms and, in particular, of domain adaptation, continual and self supervised methods, with applications in the field of computer vision, multimedia analysis and robot perception. Elisa has co-authored more than 150 scientific publications and she regularly publishes in top-tier journals and conferences in computer vision and multimedia (CVPR/ICCV/NeurIPS/ACM MM, IEEE TPAMI, IJCV, IEEE TMM, IEEE TIP). Her publications have been cited over 11,000 times and her Google Scholar H-index is 52. She has received numerous awards for her scientific activity (Honorable Mention Award ICCV 2021, Best paper award ACM MM 2015, INTEL Best Paper ICPR 2016, etc). She is a member of the editorial board of the journals Patter Recognition and Computer Vision and Image Understanding. She is/was the General Chair of ICMR 2025, Program Chair of ECCV 2024, ACM MM 2020, the Diversity Chair of ACM MM 2022, Track Chair of ICPR 2020, Special Session Chair at ICME 2022. Since 2023 she is member of the ICRA Conference Editorial Board as Editor of the Visual Perception and Learning Area. She was/is Area Chair at CVPR 2024, NeurIPS 2023, ECCV 2016, ICCV 2017WACV 2021, AISTATS 2021, BMVC 2018-2020, ICMR 2019, Senior Program Committee member of IJCAI 2019, ACM Multimedia 2016-2023, and Associate Editor at ICRA 2018, 2019, 2021. She regularly serves as program committee member and reviewer for the main international conferences (CVPR, ICCV, ECCV, NeurIPS, ICLR, ACM Multimedia, IROS, ICRA, ICPR, etc) and journals (IEEE TMM, IEEE TPAMI, IJCV, CVIU, MVA, etc) in computer vision, multimedia and robotics. She is/was the Principal Investigator and/or participated to several national and international projects. Currently, at UNITN she is the local coordinator of the EU H2020 project SPRING (2020- 2023), where she leads research activities in multi-modal human behavior analysis for human robot interactions. At FBK she leads research in video analysis in the EU H2020 project MARVEL (2020-2023) and she is the technical coordinator of the EU ISFP project PRECRISIS (2023-2025). At UNITN she is also involved as senior researcher in the H2020 EU project AI4Media (2020-2022), Horizon Europe projects AI4TRUST and ELIAS. Elisa Ricci is also involved in several industrial projects and collaborations with companies, both at national and international level. She was also recently awarded with several gifts and donations within company donation programs (Snap Inc, SAP SE, Meta, Huawei, etc). She holds a US patent on “Self-adaptive Matrix Completion for Heart Rate Estimation from Face Videos under Realistic Conditions”

Sara Beery

Assistant Professor CSAIL, MIT beery@mit.edu Confirmed

Dr. Sara Beery is the Homer A. Burnell Career Development Professor in the MIT Faculty of Artificial Intelligence and Decision-Making. She was previously a visiting researcher at Google, working on large-scale urban forest monitoring as part of the Auto Arborist project. She received her PhD in Computing and Mathematical Sciences at Caltech in 2022, where she was advised by Pietro Perona and awarded the Amori Doctoral Prize for her thesis. Her research focuses on building computer vision methods that enable global-scale environmental and biodiversity monitoring across data modalities, tackling real-world challenges including geospatial and temporal domain shift, learning from imperfect data, fine-grained categories, and longtailed distributions. She partners with industry, nongovernmental organizations, and government agencies to deploy her methods in the wild worldwide. She works toward increasing the diversity and accessibility of academic research in artificial intelligence through interdisciplinary capacity building and education, and has founded the AI for Conservation slack community, serves as the Biodiversity Community Lead for Climate Change AI, founded and directs the Workshop on Computer Vision Methods for Ecology, and co-leads the NSF Global Climate Center on AI and Biodiversity Change.

Kai Han

Assistant Professor University of Hong Kong kaihanx@hku.hk Confirmed

Kai Han is an Assistant Professor in the School of Computing and Data Science at The University of Hong Kong, where he directs the Visual AI Lab. His research interests lie in computer vision, machine learning, and artificial intelligence. His current research focuses on open world learning, 3D vision, generative AI, foundation models, and their relevant fields. Previously, he was a Visiting Faculty Researcher at Google Research, an Assistant Professor in the Department of Computer Science at the University of Bristol, and a Postdoctoral Researcher in the Visual Geometry Group (VGG) at the University of Oxford. He received his Ph.D. degree in the Department of Computer Science at The University of Hong Kong. During his Ph.D., he also worked at the WILLOW team of Inria Paris and École Normale Supérieure (ENS) in Paris. He serves as Area Chair for CVPR, ECCV, ICLR, etc.

Francesco Locatello

Assistant Professor Institute of Science and Technology Austria Francesco.Locatello@ist.ac.at
Confirmed

Before, he was a Senior Applied Scientist at Amazon Web Services (AWS). He led the Causal Representation Learning research team, where he pursued fundamental research in machine learning, artificial intelligence, and causality. He received his Ph.D. in Computer Science from ETH Zurich (2020), supervised by Gunnar Rätsch (ETH Zurich) and Bernhard Schölkopf (Max Planck Institute for Intelligent Systems), where he was awarded the ETH medal for outstanding doctoral dissertation. During his Ph.D., he was supported by a Google Fellowship and was a Fellow at the Max Planck ETH Center for Learning Systems and ELLIS. During that time, he spent one year at the Max Planck Institute for Intelligent Systems and two years at Google Brain across Zurich (1.5 years as a part-time Research Consultant) and Amsterdam (6 months internship). He holds a Computer Science M.Sc. degree from ETH Zurich and a B.Sc. engineering degree (cum laude) in Information Technologies from the University of Padua (Italy). His research on machine learning and artificial intelligence has received awards at several premier conferences and workshops, most notably the best paper award at the International Conference on Machine Learning and the Hector foundation award for outstanding achievements in machine learning from the Heidelberg Academy of Science. He co-organized multiple conferences, including the Causal Learning and Reasoning conference from 2022-2024 (sponsorship, general, and program chair); he is the program chair at the NeurIPS 2024 Benchmark and Dataset track and Volunteer chair at ICLR 2024. He also co-organized multiple workshops at ELLIS, NeurIPS, ICLR, CVPR, UAI, and a CVPR tutorial.

Eric Granger

Professor ETS Montreal Canada eric.granger@etsmtl.ca
Confirmed

Eric Granger is a Full Professor in the Dept. of Systems Engineering at ÉTS, and Director of the Laboratoire d’imagerie, de vision et d’intelligence artificielle (LIVIA). He is the FRQS Co-Chair in AI and Digital Health, and the ETS industrial research co-chair on embedded neural networks for intelligent connected buildings (Distech Controls Inc.). Prior to joining ÉTS, he completed a Ph.D. in Electrical Engineering from Polytechnique Montréal in 2001, and worked as a Defense Scientist at DRDC Ottawa (1999-2001), and in R&D at Mitel Networks (2001-04). His research interests include machine learning, pattern recognition and computer vision, with applications in affective computing, biometrics, medical imaging, and video analytics and surveillance. To date, Dr. Granger has (co- )authored 250+ peer-reviewed papers and (co-)supervised 65+ HQPs in these areas of research. He is an associate editor for Pattern Recognition (Elsevier) and the Journal on Image and Video Processing (EURASIP), on the program and organizing committees, and a reviewer for several topranking conferences (acceptance rate < 25%) and journals (IF > 5), and on the scientific and review committees of Canadian and international funding agencies. Eric has an extensive industrial experience in developing robust models and has been continuously publishing high quality papers in domain adaptation and generalization.

Aditi Raghunathan

Assistant Professor Carnegie Mellon University USA aditirag@andrew.cmu.edu
Confirmed

I am an Assistant Professor in the Computer Science Department at Carnegie Mellon University. I am also affiliated with the Machine Learning Department. I work broadly in machine learning and my goal is to make machine learning more reliable and robust. My work spans both theory and practice, and leverages tools and concepts from statistics, convex optimization, and algorithms to improve the robustness of modern systems based on deep learning. My group’s research is generously supported by Schmidt Futures, Apple, Google, and Open Philanthropy. Until recently, I was a postdoc at Berkeley AI Research. I received my PhD from Stanford University in 2021 where I was fortunate to be advised by Percy Liang. My thesis won the Arthur Samuel Best Thesis award at Stanford. My PhD was supported by the Google PhD Fellowship in Machine Learning, and an Open Philanthropy AI Fellowship. Previously, I obtained my BTech in Computer Science from IIT Madras in 2016.

Yasuhiro Tsuchida

AWL

Title: Advancing Edge AI and Video Analysis Technology: AWL’s Global Impact and Real-World Implementation.

Abstract: Founded in Tokyo in June 2016, AWL has expanded its research and development footprint to Bangalore, India, and Hanoi, Vietnam, focusing on the global advancement and social implementation of edge AI and video analysis technology.

Recent developments in applications such as Agent AI have significantly enhanced human task support. However, these applications often require costly GPU servers, presenting substantial barriers in terms of cost and power consumption.

AWL addresses these challenges with its core technology, the “AWL Engine,” and associated video analysis AI applications. These innovations aim to deliver AI solutions for video analysis that are both cost-effective and energy-efficient.

The AWL Engine is designed to overcome the difficulties of AI model optimization and accuracy degradation due to environmental changes. Miniaturized AI models often lose generalization accuracy, necessitating fine-tuning for specific installation environments. However, these models are prone to significant accuracy deterioration during operation. The AWL Engine continuously monitors AI model performance, collects data for fine-tuning, and automates the fine-tuning process to maintain optimal accuracy. Additionally, it establishes an infrastructure for deploying low-cost, low-power AI applications by leveraging federated learning to continuously improve the foundation model without compromising privacy.

This keynote will explore the core technology of the AWL Engine, its diverse applications, and the current status of its real-world implementation in retail and manufacturing sectors.

Profile: Yasuhiro Tsuchida, the visionary Director and CTO of AWL, stands at the helm of the company’s global Research and Development on Artificial Intelligence Technology. With an unparalleled ability to craft analytical frameworks that drive the company’s success, Yasuhiro sensed the dawn of a revolutionary AI era and joined AWL to lead the charge from his hometown of Hokkaido. His mission: to conquer the global market and establish AWL as a titan in the AI industry. At AWL, Yasuhiro masterminded the creation and nationwide deployment of AWLBOX and AWL Lite, now operational in over 10,000 locations.

Before his transformative journey with AWL, Yasuhiro held prestigious leadership roles at Matsushita Electric Industrial Co., Ltd., now known as Panasonic Corporation. His tenure included a remarkable five-year stint as Director of New Business Development at Panasonic Silicon Valley LAB, where he accelerated the evolution of mobile O2O Commerce. Prior to this, Yasuhiro spearheaded numerous groundbreaking projects in the Corporate R&D Division, solidifying his reputation as a pioneer of innovation.

Yasuhiro’s illustrious career began at Mobile Communications Company, where he developed a cutting-edge middleware platform for NTT DoCoMo, Japan’s largest carrier. He holds a master’s degree in Computer Science from Hokkaido University Graduate School, a testament to his profound expertise and relentless pursuit of excellence.