Parallel Multi-Scale Deep Supervision Net for Hand Key Point Detection

Key point detection plays an important role in a wide range of applications. However, predicting key points of small objects such as human hands is a challenging problem. Recent works fuse feature maps of deep Convolutional Neural Networks (CNNs), either via multi-level feature integration or multi-resolution aggregation. Despite achieving some success, the feature fusion approaches increase the complexity and the opacity of CNNs. To address this issue, we propose a novel CNN model named Parallel Multi-Scale Deep Supervision Network (P-MSDSNet) that learns feature maps at different scales in parallel with deep supervisions to produce spatial attention maps for adaptive feature propagation from layer to layer. PMSDSNet has a multi-stage with a parallel structure that fuses multi-scale features from both the same and different depth levels. The deep supervision with spatial attention would enhance relevant features and help improve the transparency of the feature learning at each stage. In the experiment, we show that P-MSDSNet outperforms the state-of-the-art approaches on benchmark datasets while requiring fewer parameters. We also demonstrate the applicability of P-MSDSNet to quantifying finger-tapping hand movements in a neuroscience study.