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.

翻译：最近的著作表明,利用通过进化神经网络(CNN)深层学到的多尺度特征对准确的轮廓探测具有极其重要的意义,本文件提出了一种新颖的预测方法,预测在两个基本方面,即多尺度地貌生成和聚合方面提高最新状态的轮廓,不同于以前直接考虑从CNN初级结构的内层获得的多尺度地貌图的作品,我们采用了一种等级深层次的模型,产生更丰富和互补的演示。此外,为了改进和有力地融合在不同尺度上所学到的演示,提出了新的 " 注意-Gated条件随机场 " (AG-CRFs),在两种公开的数据集(BSDS500和NYUDUDv2)上进行的实验显示了潜在的AG-CRF模型和总体等级框架的有效性。