InterNet plus : A Light Network for Hand Pose Estimation

Hand pose estimation from RGB images has always been a difficult task, owing to the incompleteness of the depth information. Moon et al. improved the accuracy of hand pose estimation by using a new network, InterNet, through their unique design. Still, the network still has potential for improvement. Based on the architecture of MobileNet v3 and MoGA, we redesigned a feature extractor that introduced the latest achievements in the field of computer vision, such as the ACON activation function and the new attention mechanism module, etc. Using these modules effectively with our network, architecture can better extract global features from an RGB image of the hand, leading to a greater performance improvement compared to InterNet and other similar networks.

Automated summary

The authors proposed a new network, InterNet, to improve hand pose estimation accuracy, but there is still room for improvement. By redesigning a feature extractor based on the architecture of MobileNet v3 and MoGA, and introducing the latest achievements in computer vision, the authors achieved greater performance improvement compared to InterNet and other networks.