Topic: human vision

Retinal projections often poorly represent the structure of the physical world: well-defined boundaries within the eye may correspond to irrelevant features of the physical world, while critical features of the physical world may be nearly invisible at the retinal projection. Visual cortex is equipped with specialized mechanisms for sorting these two types of features according to their utility in interpreting the scene, however we know little or nothing about their perceptual computations. I will present novel paradigms for the characterization of these processes in human vision, alongside ex

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 app

Deep neural networks (DNNs) are extremely vulnerable to adversarial attacks, where small changes to the input, unnoticeable to humans, can drastically alter the model’s predictions. One reason for their poor performance is that they process as much information as possible and make decisions based on the input received. Human vision performs differently. It tries be as efficient with information as possible while using predictions to fill in missing information based on existing knowledge. The brain may even suppress sensory input to save energy if it is familiar (i.e., predictable). As the kn