  Recent works have shown that exploiting multi-scale representations deeply
learned via convolutional neural networks (CNN) is of tremendous importance for
accurate contour detection. This paper presents a novel approach for predicting
contours which advances the state of the art in two fundamental aspects, i.e.
multi-scale feature generation and fusion. Different from previous works
directly consider- ing multi-scale feature maps obtained from the inner layers
of a primary CNN architecture, we introduce a hierarchical deep model which
produces more rich and complementary representations. Furthermore, to refine
and robustly fuse the representations learned at different scales, the novel
Attention-Gated Conditional Random Fields (AG-CRFs) are proposed. The
experiments ran on two publicly available datasets (BSDS500 and NYUDv2)
demonstrate the effectiveness of the latent AG-CRF model and of the overall
hierarchical framework.
