Edge computing and artificial intelligence (AI), especially deep learning algorithms, are
gradually intersecting to build the novel system, namely edge intelligence. However, the …

DNNs are becoming less and less over-parametrised due to recent advances in efficient
model design, through careful hand-crafted or NAS-based methods. Relying on the fact that …

Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

Rotated object detection aims to identify and locate objects in images with arbitrary
orientation. In this scenario, the oriented directions of objects vary considerably across …

Convolutional layers are one of the basic building blocks of modern deep neural networks.
One fundamental assumption is that convolutional kernels should be shared for all …

We present a general learning-based solution for restoring images suffering from spatially-
varying degradations. Prior approaches are typically degradation-specific and employ the …

Current CNN-based super-resolution (SR) methods process all locations equally with
computational resources being uniformly assigned in space. However, since missing details …

Adaptive inference is an effective mechanism to achieve a dynamic tradeoff between
accuracy and computational cost in deep networks. Existing works mainly exploit …

Recently, numerous handcrafted and searched networks have been applied for semantic
segmentation. However, previous works intend to handle inputs with various scales in pre …

Machine learning applications such as finance and medicine demand accurate and
justifiable predictions, barring most deep learning methods from use. In response, previous …