ISCC Webinar with Roland Fleming

How We Learn To See “Stuff"

Humans excel at visually recognizing materials and inferring their properties. Without touching surfaces, we can usually tell what they would feel like, and we enjoy vivid visual intuitions about how they typically behave. This is impressive because the retinal image that the visual system receives as input is the result of complex interactions between many physical processes, which the brain must somehow disentangle. I will present some work in which we show that an unsupervised neural network trained on images of surfaces spontaneously learns to disentangle reflectance, lighting and shape. However, the disentanglement is not perfect, and we find that as a result the network not only predicts the broad successes of human gloss perception, but also the specific pattern of errors that humans exhibit on an image-by-image basis. I will argue this has important implications for thinking about appearance and vision more broadly.

Bio:

Roland Fleming studied at Oxford and MIT.  After a postdoc at the Max Planck Institute for Biological Cybernetics, he joined Giessen University, where he is currently the Kurt Koffka Professor of Experimental Psychology and the Executive Director of the Center for Mind, Brain and Behaviour of the Universities of Marburg and Giessen.  His research combines psychophysics, neural modelling, computer graphics and image analysis to understand how the brain estimates the physical properties of objects and materials. He has been awarded the Young Investigator Award of the Vision Sciences Society, and had an ERC Consolidator Grant on shape perception. In 2022 he was elected Fellow of the Royal Society of Biology.