SCGNet: Shifting and Cascaded Group Network

Abstract

Many lightweight networks have been proposed for resource-limited applications, however, they cannot be efficiently applied to neural-network processing units (NPUs) due to the limited operations supported by the NPUs, and few works focus on efficient network design on the NPUs. The basic blocks of networks such as MobileNetV2 and RegNet use smaller convolution kernels with relatively small receptive fields, which are not conducive to capturing large-scale spatial information. To address this weakness, we propose Shifting and Cascaded Group (SCG) block, where we cascade group convolutions with larger kernels to exploit multi-scale information and propose shifting group convolution to communicate channel information between different groups. Besides, we carefully devise our architecture guided by some principles and finally build a very efficient network called Shifting and Cascaded Group Network (SCGNet) on NPUs. To verify the superiority of our method, we conduct extensive experiments on various tasks including image classification, object detection, human pose estimation, person re-identification, and semantic segmentation to comprehensively evaluate the performance. Results on widely used datasets such as ImageNet, PASCAL VOC, COCO, MPII, Market-1501, DukeMTMC-ReID, CUHK03, and Cityscapes demonstrate that the proposed network is a more effective network on the corresponding vision tasks.