It seems that the primary contribution of this technique is that it uses specific assumptions supported by neuroscience research in order to allow for composability of learning and better generalization. By introducing these specific assumptions (e.g. contours define objects), they are able to reduce the complexity that the model has to learn and thereby reduce the amount of data that it needs.
Obviously, the question then becomes: what happens when you have visual situations that violate or come close to violating the assumptions made?
I'm not familiar enough with the specifics of RCNs to be able to answer this; maybe someone else can. Very interesting paper and approach regardless.
Obviously, the question then becomes: what happens when you have visual situations that violate or come close to violating the assumptions made?
I'm not familiar enough with the specifics of RCNs to be able to answer this; maybe someone else can. Very interesting paper and approach regardless.