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Research on understanding and building artificially intelligent systems has moved from symbolic approaches to statistical learning, and is now beginning to study interventional models relying on concepts of causality. Some of the hard open problems of machine learning and AI are intrinsically related to causality, and progress may require advances in our understanding of how to model and infer causality from data, as well as conceptual progress on what constitutes a causal representation and a causal world model.

Multimodal learning is an emerging field at the intersection of machine learning and multimodal data processing. Information from multiple sources — such as vision, text, audio, and different sensor data — is integrated to build more robust, adaptive, and reliable models for different real-world applications. This tutorial will provide a comprehensive overview of the foundations, recent advancements, and open challenges in this domain.

3D objects are commonly modeled as polygonal meshes. Meshes describe the surface using a set of vertices, their positions in 3D space and incidence relation among vertices that compose edges and faces. Other mesh attributes include texture coordinate, normal, etc. Mesh compression has been an active research topic for over a decade now. However, growing demand from areas like gaming, animation, AR and VR, online commerce and cultural heritage applications has created the need for efficient coding technologies with reduced network bandwidths and optimal rate distortion performance.

A super resolution (SR) image refers to the image with enhanced resolutions that depict increased clarity, sharpening and details (usually with four-fold (×4)) without compromising its original contents or characters. SR is employed to reveal an image in fine details. While SR image can be produced by the emerging optical devices, e.g. super-resolution microscopy (SRM) with the change of optical resolution from ~250nm to ~10nm, computational models appear to be more variable, in particular with the advances of current state of the art artificial intelligence (AI) techniques.

The proposed tutorial on Video-Based Dynamic Mesh Coding (V-DMC) begins with an introduction to the need for efficient compression of dynamic 3D meshes, emphasizing the challenges in volumetric media applications and the role of MPEG in addressing these through standardization.

In this tutorial, we will explore the burgeoning landscape of synthetic realities, their impact, technological advancements, and ethical quandaries. Synthetic realities provide innovative solutions and opportunities for immersive experiences in various sectors, including education, healthcare, and commerce. However, these advances also present substantial challenges, such as the propagation of misinformation, privacy concerns, and ethical dilemmas.

The tutorial will be based on https://www.nowpublishers.com/article/Details/CGV-112 ArXiv version is available at: https://arxiv.org/abs/2403.18103 The astonishing growth of generative tools in recent years has empowered many exciting applications in text-to-image generation and text-to-video generation. The underlying principle behind these generative tools is the concept of diffusion, a particular sampling mechanism that has overcome some longstanding shortcomings in previous approaches.

Section: Events

Dr. Abhishek Mahesh Appaji is a is a distinguished educator, researcher, and administrator whose work focuses on "frugal innovation"—creating diagnostic tools that can function in resource-constrained environments.

ICIP 2026 arrives in Tampere, Finland! Explore the "Home of Imaging," where global researchers and industry leaders unite to shape the future of image processing. Join us.