Currently, with the rapid development of deep learning, deep neural networks (DNNs) have
been widely applied in various computer vision tasks. However, in the pursuit of …

Recent years have witnessed an exponential increase in the use of mobile and embedded
devices. With the great success of deep learning in many fields, there is an emerging trend …

We present an efficient high-resolution network, Lite-HRNet, for human pose estimation. We
start by simply applying the efficient shuffle block in ShuffleNet to HRNet (high-resolution …

High-resolution representations are essential for position-sensitive vision problems, such as
human pose estimation, semantic segmentation, and object detection. Existing state-of-the …

In this paper, we address the semantic segmentation task with a new context aggregation
scheme named object context, which focuses on enhancing the role of object information …

In this paper, we are interested in the human pose estimation problem with a focus on
learning reliable high-resolution representations. Most existing methods recover high …

Abstract In standard Convolutional Neural Networks (CNNs), the receptive fields of artificial
neurons in each layer are designed to share the same size. It is well-known in the …

Current network architecture design is mostly guided by the indirect metric of computation
complexity, ie, FLOPs. However, the direct metric, such as speed, also depends on the other …

Due to the limitations of single-source data, joint classification using multisource remote
sensing data has received increasing attention. However, existing methods still have certain …

Abstract Computer-Aided Diagnosis (CAD) for dermatological diseases offers one of the
most notable showcases where deep learning technologies display their impressive …