论文标题
人类解析的亲和力感知压缩和扩展网络
Affinity-aware Compression and Expansion Network for Human Parsing
论文作者
论文摘要
作为一项细粒度的细分任务，由于模棱两可的定义和相似的人类部位之间的关系，人类解析仍然面临两个挑战：彼此之间的互相启示和部分内部的矛盾。为了解决这两个问题，本文提出了一个新颖的\ textIt {affinity-ware敏感压缩和扩展}网络（acenet），该网络主要由两个模块组成：局部压缩模块（LCM）和全局扩展模块（GEM）。具体而言，LCM通过从额外的骨架分支获得的结构骨架点压缩零件相关信息。它可以减少部分间的干扰，并增强模棱两可部分之间的结构关系。此外，GEM通过将空间亲和力与边界指南结合在一起，将每个部分的语义信息扩展到完整的部分中，这也可以有效地增强零件的语义一致性。 Acenet在具有挑战性的唇部和Pascal-Part Part数据集上实现了新的最新性能。特别是，在唇部基准上实现了58.1％的含义。
As a fine-grained segmentation task, human parsing is still faced with two challenges: inter-part indistinction and intra-part inconsistency, due to the ambiguous definitions and confusing relationships between similar human parts. To tackle these two problems, this paper proposes a novel \textit{Affinity-aware Compression and Expansion} Network (ACENet), which mainly consists of two modules: Local Compression Module (LCM) and Global Expansion Module (GEM). Specifically, LCM compresses parts-correlation information through structural skeleton points, obtained from an extra skeleton branch. It can decrease the inter-part interference, and strengthen structural relationships between ambiguous parts. Furthermore, GEM expands semantic information of each part into a complete piece by incorporating the spatial affinity with boundary guidance, which can effectively enhance the semantic consistency of intra-part as well. ACENet achieves new state-of-the-art performance on the challenging LIP and Pascal-Person-Part datasets. In particular, 58.1% mean IoU is achieved on the LIP benchmark.
