Human parsing, which aims at resolving human body and clothes into semantic part regions from an human image, is a fundamental task in human-centric analysis. Recently, the approaches for human parsing based on deep convolutional neural networks (DCNNs) have made significant progress. However, hierarchically exploiting multiscale and spatial contexts as convolutional features is still a hurdle to overcome. In order to boost the scale and spatial awareness of a DCNN, we propose two effective structures, named “Attention SPP and Attention RefineNet,” to form a Mutual Attention operation, to exploit multiscale and spatial semantics different from the existing approaches. Moreover, we propose a novel Attention Guidance Network (AG-Net), a simple yet effective architecture without using bells and whistles (such as human pose and edge information), to address human parsing tasks. Comprehensive evaluations on two public datasets well demonstrate that the AG-Net outperforms the state-of-the-art networks.

中文翻译：

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多尺度满足空间意识：用于人类解析的高效注意力指导网络

旨在以人体图像将人体和衣服分解为语义部分区域的人体分析是以人为本的分析的一项基本任务。近来，基于深度卷积神经网络（DCNN）的人体解析方法取得了重大进展。但是，将多尺度和空间上下文作为卷积特征进行分层开发仍然是要克服的障碍。为了增强DCNN的规模和空间意识，我们提出了两个有效的结构，即“ Attention SPP和Attention RefineNet”，以形成相互注意操作，以利用不同于现有方法的多尺度和空间语义。此外，我们提出了一种新颖的注意力引导网络（AG-Net），它是一种简单而有效的架构，无需使用铃声和口哨声（例如人体姿势和边缘信息），解决人工解析任务。对两个公共数据集的综合评估很好地证明了AG-Net胜过了最新的网络。