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Recently, fully convolutional neural networks (FCNs) have shown significantperformance in image parsing, including scene parsing and object parsing.Different from generic object parsing tasks, hand parsing is more challengingdue to small size, complex structure, heavy self-occlusion and ambiguoustexture problems. In this paper, we propose a novel parsing framework,Multi-Scale Dual-Branch Fully Convolutional Network (MSDB-FCN), for handparsing tasks. Our network employs a Dual-Branch architecture to extractfeatures of hand area, paying attention on the hand itself. These features areused to generate multi-scale features with pyramid pooling strategy. In orderto better encode multi-scale features, we design a Deconvolution and BilinearInterpolation Block (DB-Block) for upsampling and merging the features ofdifferent scales. To address data imbalance, which is a common problem in manycomputer vision tasks as well as hand parsing tasks, we propose ageneralization of Focal Loss, namely Multi-Class Balanced Focal Loss, to tackledata imbalance in multi-class classification. Extensive experiments onRHD-PARSING dataset demonstrate that our MSDB-FCN has achieved thestate-of-the-art performance for hand parsing.

Multi-Scale Dual-Branch Fully Convolutional Network for Hand Parsing