I am investigating what role recurrency plays in vision as opposed to purely feed-forward connections. The brain has connections all over the place but yet most of our today’s machine learning algorithms in object recognition operate in only a feed-forward way.
So why is recurrency important?
One application that we have found is the recognition of occluded objects (see Publications). Here, recurrency enables the integration of spatial information and it also allows for fewer weights because this integration seems to be similar across timesteps.
I am also investigating whether recurrency could account for an effect of visual context where we can recognize difficult objects with more ease when knowing about their surroundings (e.g. a bank vault door in isolation is likely difficult to recognize but in the context of a bank with cashiers and money, it might be much simpler).