  We propose a novel attention model that can accurately attends to target
objects of various scales and shapes in images. The model is trained to
gradually suppress irrelevant regions in an input image via a progressive
attentive process over multiple layers of a convolutional neural network. The
attentive process in each layer determines whether to pass or block features at
certain spatial locations for use in the subsequent layers. The proposed
progressive attention mechanism works well especially when combined with hard
attention. We further employ local contexts to incorporate neighborhood
features of each location and estimate a better attention probability map. The
experiments on synthetic and real datasets show that the proposed attention
networks outperform traditional attention methods in visual attribute
prediction tasks.
