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Invited speaker at The Skin of Things online art-science conference

A photograph or painting of a glazed vase might consist of irregularly-shaped bright patches, small white dots, and large low-contrast gradients—yet we immediately see these as reflections on the glossy surface, sharp highlights, and the smooth …

Symposium organiser and speaker at TeaP 2021

Deep neural networks (DNNs) have revolutionised computer vision, often now recognising objects and faces as well as humans can. An initial wave of fMRI and electrophysiological studies around 2015 showed that features in object-recognition-trained …

Invited talk, Vrije Universiteit Amsterdam

Models of vision have come far in the past 10 years. Deep neural networks can recognise objects with near-human accuracy, and predict brain activity in high-level visual regions. However, most networks require supervised training using ground-truth …

Invited talk, MPI Leipzig

Computational visual neuroscience has come a long way in the past 10 years. For the first time, we have fully explicit, image-computable models that can recognise objects with near-human accuracy, and predict brain activity in high-level visual …

VSS 2020 accepted poster (conference cancelled)

Despite the impressive achievements of supervised deep neural networks, brains must learn to represent the world without access to ground-truth training data. We propose that perception of distal properties arises instead from unsupervised learning …

Level Up Human podcast appearance

Level Up Human is a podcast panel show, in which scientists compete to pitch improvements to the human design. In this episode, I pitch a bugfix for human vision: the ability to see the polarisation of light.

#neuromatch2020 talk

Perceiving the glossiness of a surface is a challenging visual inference that requires disentangling the contributions of reflectance, lighting, and shape to the retinal image. How do our visual systems develop this ability? We suggest that brains …

Trinity College Dublin invited lecture in series: Deep Learning Meets Neuroscience

I will talk about two projects in which we use unsupervised deep learning, combined with large computer-rendered stimulus sets, as a framework to understand how brains learn rich scene representations without ground-truth world information. By …

Summer school lecture: Unsupervised Learning of Object Material and Shape from Images and Videos

When DNNs are trained on ground-truth information, they can reach or exceed human performance in many visual tasks. However, no equivalent to these massive labelled training sets exists during human visual development. I will talk about two projects …

ECVP talk: Learning about Shape, Material, and Illumination by Predicting Videos

Unsupervised deep learning provides a framework for understanding how brains learn rich scene representations without ground-truth world information. We rendered 10,000 videos of irregularly-shaped objects moving with random rotational axis, speed, …