  Visual recognition systems mounted on autonomous moving agents face the
challenge of unconstrained data, but simultaneously have the opportunity to
improve their performance by moving to acquire new views of test data. In this
work, we first show how a recurrent neural network-based system may be trained
to perform end-to-end learning of motion policies suited for this "active
recognition" setting. Further, we hypothesize that active vision requires an
agent to have the capacity to reason about the effects of its motions on its
view of the world. To verify this hypothesis, we attempt to induce this
capacity in our active recognition pipeline, by simultaneously learning to
forecast the effects of the agent's motions on its internal representation of
the environment conditional on all past views. Results across two challenging
datasets confirm both that our end-to-end system successfully learns meaningful
policies for active category recognition, and that "learning to look ahead"
further boosts recognition performance.
