Deep learning has been widely used for medical image segmentation and a large number of
papers has been presented recording the success of deep learning in the field. A …

Deep learning has made substantial breakthroughs in many fields due to its powerful
automatic representation capabilities. It has been proven that neural architecture design is …

Recent studies on mobile network design have demonstrated the remarkable effectiveness
of channel attention (eg, the Squeeze-and-Excitation attention) for lifting model performance …

Recently, Vision Transformers (ViTs) have been broadly explored in visual recognition. With
low efficiency in encoding fine-level features, the performance of ViTs is still inferior to the …

To reduce the human efforts in neural network design, Neural Architecture Search (NAS)
has been applied with remarkable success to various high-level vision tasks such as …

Convolution exploits locality for efficiency at a cost of missing long range context. Self-
attention has been adopted to augment CNNs with non-local interactions. Recent works …

Recent work on automated augmentation strategies has led to state-of-the-art results in
image classification and object detection. An obstacle to a large-scale adoption of these …

In this paper, we study the context aggregation problem in semantic segmentation.
Motivated by that the label of a pixel is the category of the object that the pixel belongs to, we …

The semantic image segmentation task consists of classifying each pixel of an image into an
instance, where each instance corresponds to a class. This task is a part of the concept of …

Bottom-up human pose estimation methods have difficulties in predicting the correct pose for
small persons due to challenges in scale variation. In this paper, we present HigherHRNet: a …