  Deep neural network (DNN) based machine perception frameworks process the
entire input in a one-shot manner to provide answers to both "what object is
being observed" and "where it is located". In contrast, the "two-stream
hypothesis" from neuroscience explains the neural processing in the human
visual cortex as an active vision system that utilizes two separate regions of
the brain to answer the what and the where questions. In this work, we propose
a machine learning framework inspired by the "two-stream hypothesis" and
explore the potential benefits that it offers. Specifically, the proposed
framework models the following mechanisms: 1) ventral (what) stream focusing on
the input regions perceived by the fovea part of an eye (foveation), 2) dorsal
(where) stream providing visual guidance, and 3) iterative processing of the
two streams to calibrate visual focus and process the sequence of focused image
patches. The training of the proposed framework is accomplished by label-based
DNN training for the ventral stream model and reinforcement learning for the
dorsal stream model. We show that the two-stream foveation-based learning is
applicable to the challenging task of weakly-supervised object localization
(WSOL), where the training data is limited to the object class or its
attributes. The framework is capable of both predicting the properties of an
object and successfully localizing it by predicting its bounding box. We also
show that, due to the independent nature of the two streams, the dorsal model
can be applied on its own to unseen images to localize objects from different
datasets.
