Convolutional neural networks (CNNs) have been applied to visual tasks since the late
1980s. However, despite a few scattered applications, they were dormant until the mid …

Deep convolutional neural networks have been widely used in numerous applications, but
their demanding storage and computational resource requirements prevent their …

Recently, transformer has achieved remarkable performance on a variety of computer vision
applications. Compared with mainstream convolutional neural networks, vision transformers …

Attention mechanism, especially channel attention, has gained great success in the
computer vision field. Many works focus on how to design efficient channel attention …

Deploying convolutional neural networks (CNNs) on embedded devices is difficult due to the
limited memory and computation resources. The redundancy in feature maps is an important …

Image reconstruction and synthesis have witnessed remarkable progress thanks to the
development of generative models. Nonetheless, gaps could still exist between the real and …

Learning portable neural networks is very essential for computer vision for the purpose that
pre-trained heavy deep models can be well applied on edge devices such as mobile …

Deploying convolutional neural networks (CNNs) on mobile devices is difficult due to the
limited memory and computation resources. We aim to design efficient neural networks for …

Remote photoplethysmography (rPPG) technology has drawn increasing attention in recent
years. It can extract Blood Volume Pulse (BVP) from facial videos, making many applications …

Searching techniques in most of existing neural architecture search (NAS) algorithms are
mainly dominated by differentiable methods for the efficiency reason. In contrast, we develop …