Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional …

Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

In this paper, we propose a conceptually simple but very effective attention module for
Convolutional Neural Networks (ConvNets). In contrast to existing channel-wise and spatial …

The recently developed vision transformer (ViT) has achieved promising results on image
classification compared to convolutional neural networks. Inspired by this, in this paper, we …

We study how to introduce locality mechanisms into vision transformers. The transformer
network originates from machine translation and is particularly good at modelling long-range …

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 …

Attention mechanisms, which enable a neural network to accurately focus on all the relevant
elements of the input, have become an essential component to improve the performance of …

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

Feature fusion, the combination of features from different layers or branches, is an
omnipresent part of modern network architectures. It is often implemented via simple …

We propose a universal building block of Convolutional Neural Network (ConvNet) to
improve the performance without any inference-time costs. The block is named Diverse …