Abstract
Recently, fully convolutional neural networks (FCNs) have shown significant performance in image parsing, including scene parsing and object parsing. Different from generic object parsing tasks, hand parsing is more challenging due to small size, complex structure, heavy self-occlusion and ambiguous texture problems. In this paper, we propose a novel parsing framework, Multi-Scale Dual-Branch Fully Convolutional Network (MSDB-FCN), for hand parsing tasks. Our network employs a Dual-Branch architecture to extract features of hand area, paying attention on the hand itself. These features are used to generate multi-scale features with pyramid pooling strategy. In order to better encode multi-scale features, we design a Deconvolution and Bilinear Interpolation Block (DB-Block) for upsampling and merging the features of different scales. To address data imbalance, which is a common problem in many computer vision tasks as well as hand parsing tasks, we propose a generalization of Focal Loss, namely Multi-Class Balanced Focal Loss, to tackle data imbalance in multi-class classification. Extensive experiments on RHD-PARSING dataset demonstrate that our MSDB-FCN has achieved the state-of-the-art performance for hand parsing.
Abstract (translated)
近年来，全卷积神经网络（FCN）在图像分析（包括场景分析和对象分析）中显示出显著的性能。与一般的对象解析任务不同，手工解析由于体积小、结构复杂、自封闭性强、纹理模糊等问题而具有更大的挑战性。本文提出了一种用于手工解析任务的多尺度双分支全卷积网络（msdb-fcn）解析框架。我们的网络采用双分支结构来提取手部区域的特征，注意手部本身。这些特性用于通过金字塔池策略生成多尺度特性。为了更好地对多尺度特征进行编码，我们设计了一个反褶积和双线性插值块（DB块），用于对不同尺度的特征进行上采样和合并。为了解决数据不平衡问题，这是许多计算机视觉任务和手分析任务中常见的问题，我们提出了一个焦点损失的泛化，即多级平衡焦点损失，以解决多级分类中的数据不平衡问题。对rhd解析数据集的大量实验表明，我们的msdb-fcn已经实现了最先进的手工解析性能。
URL
https://arxiv.org/abs/1905.10100
