We present an interpretation of Inception modules in convolutional neural networks as being
an intermediate step in-between regular convolution and the depthwise separable …

Deep convolutional neural networks (CNNs) are the dominant technology in computer vision
today. Much of the recent computer vision literature can be thought of as a competition to …

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 …

A number of studies have shown that increasing the depth or width of convolutional
networks is a rewarding approach to improve the performance of image recognition. In our …

Convolutional networks are at the core of most state-of-the-art computer vision solutions for
a wide variety of tasks. Since 2014 very deep convolutional networks started to become …

In recent years, deep learning has achieved great success in many computer vision
applications. Convolutional neural networks (CNNs) have lately emerged as a major …

Inspired by the long-range modeling ability of ViTs large-kernel convolutions are widely
studied and adopted recently to enlarge the receptive field and improve model performance …

Convolutional layers are the core building blocks of Convolutional Neural Networks (CNNs).
In this paper, we propose to augment a convolutional layer with an additional depthwise …

In this work, we develop a novel method for automatically learning aspects of the structure of
a deep model, in order to improve its performance, especially when labeled training data are …

In this paper, we propose to factorize the standard convolutional layer to reduce the
computation. The 3D convolution operation in a convolutional layer can be considered as …