Face recognition (FR) is one of the most popular and long-standing topics in computer
vision. With the recent development of deep learning techniques and large-scale datasets …

Existing methods of 3D dthus limiting the scope of their practical applications. In this paper,
we propose a novel regression framework which makes a balance among speed, accuracy …

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

High-resolution representation learning plays an essential role in many vision problems, eg,
pose estimation and semantic segmentation. The high-resolution network (HRNet)~\cite …

Head pose estimation suffers from several problems, including low pose tolerance under
different disturbances and ambiguity arising from common head pose representation. In this …

Conventional neural architecture search (NAS) approaches are usually based on
reinforcement learning or evolutionary strategy, which take more than 1000 GPU hours to …

Deep learning, in particular the deep convolutional neural networks, has received
increasing interests in face recognition recently, and a number of deep learning methods …

This paper proposed a Soft Filter Pruning (SFP) method to accelerate the inference
procedure of deep Convolutional Neural Networks (CNNs). Specifically, the proposed SFP …

The pursuit of more intelligent and credible autonomous systems, akin to human society, has
been a long-standing endeavor for humans. Leveraging the exceptional reasoning and …

Most person re-identification (re-ID) approaches are based on supervised learning, which
requires intensive manual annotation for training data. However, it is not only …