Convolutional Neural Networks (CNNs) have revolutionized image classification by
extracting spatial features and enabling state-of-the-art accuracy in vision-based tasks. The …

Convolutional neural networks have spatial representations which read patterns in the
vision tasks. Squeeze and excitation links the channel wise representations by explicitly …

We propose an end-to-end-trainable attention module for convolutional neural network
(CNN) architectures built for image classification. The module takes as input the 2D feature …

Convolutional Neural Networks (CNNs) have advanced significantly in visual representation
learning and recognition. However, they face notable challenges in performance and …

Convolutional neural networks are built upon the convolution operation, which extracts
informative features by fusing spatial and channel-wise information together within local …

In this work, we propose a generally applicable transformation unit for visual recognition with
deep convolutional neural networks. This transformation explicitly models channel …

Convolutional Neural Networks (CNNs) are powerful models that achieve impressive results
for image classification. In addition, pre-trained CNNs are also useful for other computer …

Recently, many researches have demonstrated that the attention mechanism has great
potential in improving the performance of deep convolutional neural networks (CNNs) …

Much of the recent progress made in image classification research can be credited to
training procedure refinements, such as changes in data augmentations and optimization …

In recent years, computer vision has witnessed remarkable advancements in image
classification, specifically in the domains of fully convolutional neural networks (FCNs) and …