Short Courses

Short courses are open to both ICIP 2026 conference attendees and participants who wish to enroll in the courses independently. Conference registration is not required.

Interested participants are invited to begin the registration process via the registration page and complete their booking by paying the course fee.

The following short courses will be offered at ICIP 2026:

Presenter:
– Paul A. Rodriguez, Pontificia Universidad Católica del Perú, Perù

While the impact of convolutional neural networks (CNN) / deep learning (DL) / artificial intelligence (AI) is still being assessed in several everyday technological and ethical aspects of our societies, stochastic optimizers, which encompass the stochastic gradient descent (SGD) and variants (e.g. Momentum, ADAM, AdamW, etc.), and the selection of their associated hyperparameters, play a crucial role in the successful training of such models.

The SGD algorithm, which may be succinctly explained as the classical gradient descent (GD) algorithm along with a (very) noisy gradient, has two hyperparameters, i.e. the learning rate (LR) and the batch-size (BSz), which directly affect the practical rate of convergence as well as the overall model’s performance. However, more effective (and popular) algorithms, such as Momentum, ADAM, AdamW and derived methods, do have several hyperparameters, whose influence is not as direct nor as well understood as SGD’s LR and BSz.

As it will be detailed throughout this proposal, the primary objective of this six-hours course is to combined the essential theoretical aspects associated with the SGD algorithm and variants’ hyperparameters, and their influence on performance, along with hands-on experience to implement (TensorFlow or Pytorch) different methods to fine-tune the most influential hyperparameters, from grid/random search strategies to Bayesian optimization, also considering warm-start tech-niques and metaheuristic methods.

Presenters:
– Deepayan Bhowmik, Newcastle University, UK
– Touradj Ebrahimi, EPFL, Switzerland
– Frederik Temmermans, Vrije Universiteit Brussel, Belgium and imec, Belgium

The rise of technologies such as Generative AI and mobile phone cameras has led to large-scale media content creation and consumption. While this progress opens up opportunities, especially in creative industries, it also enables issues like cyber attacks, piracy, fake media distribution, and concerns around trust and privacy. Manipulated media has caused social unrest, spread political rumors, and incited hate crimes in recent years.

Media modifications are not always negative; they are now a standard part of many production pipelines and new knowledge creation. However, in many domains, creators need to declare the types of modifications performed. Failing to do so can call media trustworthiness into question or suggest an intent to hide manipulations. Such a need triggered an initiative by the JPEG committee (https://jpeg.org/) to standardise a way to annotate media assets (regardless of the intent) and securely link the assets and annotations together. This JPEG Trust standard (published in January 2025) ensures interoperability between a wide range of applications dealing with media asset creation and modification, providing a set of standard mechanisms to describe and embed information about the creation and modification of media assets.

This short course aims to present a holistic understanding of the topic, media authenticity in the age of AI, and potential solutions through state-of-the-art deepfake/manipulation detection algorithms, watermarking, and relevant standards such as JPEG Trust and the Coalition for Content Provenance and Authenticity (C2PA), along with hands-on coding exercises on some implementations and use case scenarios.

Presenter:
– Jarno Nikkanen, Valosa Imaging

Digital cameras are ubiquitous in the everyday lives of people, inside devices such as smartphones, laptops, security cameras, autonomous vehicles, and many more. The functionality and image quality of the digital cameras is largely determined by the computational algorithms that are involved in the operation of a digital camera. Knowing how the original raw image signal is controlled and modified before it is output from the digital camera can be beneficial for the engineers and researchers who use the digital camera output frames and videos as the inputs to their own developments.

This training provides a generic overview to Image Signal Processor (ISP) pipelines and algorithms, as well as Camera Control Algorithms (3A+) and related components. A simplified example ISP and algorithms are used to describe the functions that can be found in most ISPs and cameras that are commercially available. After the training, the participants should be familiar with the process that involves controlling the actuators and camera sensor to get properly focused and exposed raw images as the input to ISP, as well as turning the raw image from camera sensor into the device independent YUV/RGB frame that can be displayed and/or encoded into still image or video files. Example images, figures, diagrams, and pseudo code are used to make the presentation more concrete and practical.