  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.
