  We consider the problem of fine-grained classification on an edge camera
device that has limited power. The edge device must sparingly interact with the
cloud to minimize communication bits to conserve power, and the cloud upon
receiving the edge inputs returns a classification label. To deal with
fine-grained classification, we adopt the perspective of sequential fixation
with a foveated field-of-view to model cloud-edge interactions. We propose a
novel deep reinforcement learning-based foveation model, DRIFT, that
sequentially generates and recognizes mixed-acuity images.Training of DRIFT
requires only image-level category labels and encourages fixations to contain
task-relevant information, while maintaining data efficiency. Specifically,
wetrain a foveation actor network with a novel Deep Deterministic Policy
Gradient by Conditioned Critic and Coaching (DDPGC3) algorithm. In addition, we
propose to shape the reward to provide informative feedback after each fixation
to better guide RL training. We demonstrate the effectiveness of DRIFT on this
task by evaluating on five fine-grained classification benchmark datasets, and
show that the proposed approach achieves state-of-the-art performance with over
3X reduction in transmitted pixels.
