论文标题
C-dlinknet：考虑人类解析的多级语义特征
C-DLinkNet: considering multi-level semantic features for human parsing
论文作者
论文摘要
人解析是语义分割的重要分支，这是一项精细的语义分割任务，旨在识别人类的组成部分。人解析的挑战是提取有效的语义特征，以解决变形和多尺度变化。在这项工作中，我们提出了一个基于linknet的端到端模型，该模型包含一个名为“平滑模块”的新模块，以在解码器部分中将多级特征组合在一起。与最先进的方法相比，C-DLINKNET能够产生竞争性解析性能，该方法具有较小的输入大小，没有其他信息，即在LIP数据集的验证集中实现MIOU = 53.05。
Human parsing is an essential branch of semantic segmentation, which is a fine-grained semantic segmentation task to identify the constituent parts of human. The challenge of human parsing is to extract effective semantic features to resolve deformation and multi-scale variations. In this work, we proposed an end-to-end model called C-DLinkNet based on LinkNet, which contains a new module named Smooth Module to combine the multi-level features in Decoder part. C-DLinkNet is capable of producing competitive parsing performance compared with the state-of-the-art methods with smaller input sizes and no additional information, i.e., achiving mIoU=53.05 on the validation set of LIP dataset.
