ECCV 2026
The 19th European Conference on Computer Vision, the biennial premier research conference in computer vision and machine learning managed by the European Computer Vision Association, held at Malmo Arena and Malmomassan in Malmo, Sweden.
Deep learning applications in ophthalmology
Deep learning techniques have revolutionized the field of image analysis and played a disruptive role in the ability to quickly and efficiently train image analysis models that perform as well as human beings. This talk will cover the beginnings of the application of deep learning in the field of ophthalmology and vision science, and cover a variety of applications of using deep learning as a method for scientific discovery and latent associations.
Identity-Expression Ambiguity in 3D Morphable Face Models
3D Morphable Models are my favorite class of generative models and are commonly used to model faces. They are typically applied to ill-posed problems such as 3D reconstruction from 2D data. I'll start my presentation with an introduction into 3D Morphable Models and show what they are capable of doing. I'll then focus on our recent finding, the Identity-Expression Ambiguity: We demonstrate that non-orthogonality of the variation in identity and expression can cause identity-expression ambiguity in 3D Morphable Models, and that in practice expression and identity are far from orthogonal and can explain each other surprisingly well. Whilst previously reported ambiguities only arise in an inverse rendering setting, identity-expression ambiguity emerges in the 3D shape generation process itself. The goal of this presentation is to demonstrate the ambiguity and discuss its potential consequences in a computer vision setting as well as for understanding face perception mechanisms in the human brain.
If we can make computers play chess, why can't we make them see?
If we can make computers play chess and even Jeopardy and Go, then why can't we make them see like us? How does our brain solve the problem of seeing? I will describe some of our recent insights into understanding object recognition in the brain using behavioral, neuronal and computational methods.
Measuring relevant features of the social and physical environment with imagery
The efficacy of images to create quantitative measures of urban perception has been explored in psychology, social science, urban planning and architecture over the last 50 years. The ability to scale these measurements has become possible only in the last decade, due to increased urban surveillance in the form of street view and satellite imagery, and the accessibility of such data. This talk will present a series of projects which make use of imagery and CNNs to predict, measure and interpret the social and physical environments of our cities.
Learning to aggress – Behavioral and circuit mechanisms of aggression reward
Aggression is an ethologically complex behavior with equally complex underlying mechanisms. Here, I present data on one form of aggression, appetitive or rewarding aggression, and the behavioral, cellular and system-level mechanisms guiding this behavior. First, I will present one way in which appetitive aggression is modeled in mice, and extend aggression motivation to the concept of compulsive aggression seeking and relapse. I will then briefly highlight recent advances in computer vision and machine learning for automated scoring of aggressive behavior, the role of specific cell-types in controlling aggression reward, and close with preliminary data on the whole brain aggression reward functional connectome using light sheet fluorescent microscopy (LSFM).
Silicon retinas that make spike events
The story of event cameras starts from the very beginnings of neuromorphic engineering with Misha Mahowald and Carver Mead. The chip design of these “silicon retina” cameras is the most crucial aspect that might enable them to come to mass production and widespread use. Once we have a usable camera is just the beginning, because now we need to think of our use of the data as though we were some type of artificial “silicon cortex”. That step has just started but the last few years have brought some remarkable results from the computer vision community. This talk will have a lot of live demonstrations.
Machine Learning as a tool for positive impact : case studies from climate change
Climate change is one of our generation's greatest challenges, with increasingly severe consequences on global ecosystems and populations. Machine Learning has the potential to address many important challenges in climate change, from both mitigation (reducing its extent) and adaptation (preparing for unavoidable consequences) aspects. To present the extent of these opportunities, I will describe some of the projects that I am involved in, spanning from generative model to computer vision and natural language processing. There are many opportunities for fundamental innovation in this field, advancing the state-of-the-art in Machine Learning while ensuring that this fundamental progress translates into positive real-world impact.
Shape from shading in nature: does it provide optimal camouflage?
Blindspots in Computer Vision - How can neuroscience guide AI?
Scientists have worked to recreate human vision in computers for the past 50 years. But how much about human vision do we actually know? And can the brain be useful in furthering computer vision? This talk will take a look at the similarities and differences between (modern) computer vision and human vision, as well as the important crossovers, collaborations, and applications that define the interface between computational neuroscience and computer vision. If you want to know more about how the brain sees (really sees), how computer vision developments are inspired by the brain, or how to apply AI to neuroscience, this talk is for you.