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Keynote Lectures

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Sylvain Paris, Adobe Research, United States, United States

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Silvia Miksch, Vienna University of Technology, Austria, Austria

Seeing, Speaking, and Reasoning in a Visual World
Cees G. M. Snoek, University of Amsterdam, Netherlands, Netherlands

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Sylvain Paris
Adobe Research, United States

Brief Bio
Sylvain Paris is a Fellow and a Research Org Leader at Adobe Research. His personal research interests are about photo editing and related topics. His team covers various aspects of artificial intelligence, machine learning, computer graphics, computer vision, programming languages, and content authenticity. Several of their research contributions have become popular features in products like Photoshop, Lightroom, Firefly, and Premiere. Sylvain served several times on the program committee of conferences like SIGGRAPH, Eurographics, and CVPR, and on the editorial boards of journals like Transactions on Graphics and Transactions on Computational Imaging. In 2021, he chaired the Technical Papers program of SIGGRAPH. Before joining Adobe in 2007, he worked with François Sillion at INRIA to prepare his PhD that he received from Université Joseph Fourier in Grenoble in 2004, and he then did a post-doc at MIT with Frédo Durand.

Abstract
Adobe is in a unique position to invent and develop AI tools for creatives, from professionals to casual users. This inspires and motivates our research with high expectations on the quality of the results we produce, the complexity of the workflows we support, the usability of the tools we enable, and the way we use data. In this talk, I will present a few recent projects in imaging and related domains, and discuss how they fit in the broader context of AI for creativity. I will cover topics including content generation & editing, image understanding, model optimization, and authenticity. I will use these examples to illustrate how much progress have been done thanks to AI since it became mature enough to support practical use cases, and I will also point at challenges that remain and propose a few avenues for future research.

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Silvia Miksch
Vienna University of Technology, Austria
http://www.ifs.tuwien.ac.at/~silvia

Brief Bio
Silvia Miksch is a University Professor and head of the research unit "Visual Analytics" (Centre for Visual Analytics Science and Technology (CVAST)), which is part of Vienna University of Technology (TU Wien), Faculty of Informatics, Institute of Visual Computing and Human-Centered Technology. She served on various program committees of international scientific conferences (paper co-chair of the IEEE VAST 2010, 2011, 2020, EG EuroVis 2012, etc.), belong(ed) to the editorial board of IEEE TVCG, CGF, etc., and she act(ed) in various strategic and guiding committees, such as the VAST steering committee and the VIS Executive Committee (VEC). Currently, she is chairing the EuroVis steering committee. In 2020, she was inducted into the IEEE Visualization Academy (or in short, Vis Academy). The Vis Academy was established in 2018 by the IEEE VGTC Executive Committee. Induction into the Vis Academy is the highest and most prestigious honor in the field of visualization. In 2023 she was honored for my outstanding technical contributions to VA of time-varying data with the prestigious IEEE VGTC Visualization Technical Achievement Award. She has more than 300 scientific publications, and her main research interests are visualization and visual analytics over time and space, with a particular focus on interaction techniques, network-based, knowledge-assisted, and guidance-enriched methods. Her cross-cutting application fields are healthcare, digital humanities/arts, financial fraud detection, etc. More information can be found at: https://www.cvast.tuwien.ac.at/team/silvia-miksch

Abstract
Available Soon

Seeing, Speaking, and Reasoning in a Visual World

Cees G. M. Snoek
University of Amsterdam, Netherlands
https://www.ceessnoek.info

Brief Bio
Cees G.M. Snoek is a full professor in artificial intelligence at the University of Amsterdam, where he heads the Video & Image Sense Lab and the interdisciplinary Human-Aligned Video AI Lab. He is also a director of three public-private AI research labs with stakeholders like Qualcomm, TomTom and TNO. At University spin-off Kepler Vision Technologies he acts as Chief Scientific Officer. Professor Snoek is also scientific director of Amsterdam AI, a collaboration between government, academic, medical and other organisations in Amsterdam to study, develop and deploy responsible AI. He was previously an assistant and associate professor at the University of Amsterdam, as well as Visiting Scientist at Carnegie Mellon University, Fulbright Junior Scholar at UC Berkeley, head of R&D at University spin-off Euvision Technologies and managing principal engineer at Qualcomm Research Europe.

Abstract
Vision–language foundation models have made striking progress, yet they still fall short of forming coherent models of the world. Many systems remain linguistically narrow, visually ungrounded, and reliant on shallow, single-step reasoning, limiting their ability to generalize across languages, cultures, and complex visual scenes. In this talk, I will argue that meaningful progress requires treating seeing, speaking, and reasoning as a unified problem, grounded in visual evidence and inclusive by design. I will present recent advances that move toward this goal along two tightly connected dimensions. First, I will discuss how multilingual modeling reshapes text-to-image generation. Moving beyond translation-based pipelines, learning visual concepts directly across languages enables more faithful, culturally aligned generation while retaining strong performance and efficiency. This reframes inclusivity as a catalyst for better representations rather than a trade-off. Second, I will address the limitations of one-shot visual reasoning and introduce an approach to iterative, grounded reasoning. By explicitly linking each reasoning step to image regions and enforcing consistency between global scene understanding and local visual evidence, models can achieve more accurate, interpretable, and spatially precise reasoning across challenging visual tasks. Together, these directions outline a broader vision for visual AI: systems that can perceive the world, communicate across languages, and reason over space and time in a grounded and unified manner—moving beyond surface-level fluency toward deeper visual understanding.