Abstract
Binary determination of the presence of objects is
one of the problems where humans perform extraordinarily better than computer vision systems,
in terms of both speed and preciseness. One of the
possible reasons is that humans can skip most of
the clutter and attend only on salient regions. Recurrent attention models (RAM) are the first computational models to imitate the way humans process images via the REINFORCE algorithm. Despite that RAM is originally designed for image
recognition, we extend it and present recurrent existence determination, an attention-based mechanism
to solve the existence determination. Our algorithm
employs a novel k-maximum aggregation layer and
a new reward mechanism to address the issue of
delayed rewards, which would have caused the instability of the training process. The experimental
analysis demonstrates significant efficiency and accuracy improvement over existing approaches, on
both synthetic and real-world datasets