Humans can naturally and effectively find salient regions in complex scenes. Motivated by
this observation, attention mechanisms were introduced into computer vision with the aim of …

Finding correspondences between images is a fundamental problem in computer vision. In
this paper, we show that correspondence emerges in image diffusion models without any …

Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer
vision and pattern recognition, and underpins a diverse set of real-world applications. The …

Node classification is an important research topic in graph learning. Graph neural networks
(GNNs) have achieved state-of-the-art performance of node classification. However, existing …

A novel coronavirus (COVID-19) recently emerged as an acute respiratory syndrome, and
has caused a pneumonia outbreak world-widely. As the COVID-19 continues to spread …

Scaling up deep neural network capacity has been known as an effective approach to
improving model quality for several different machine learning tasks. In many cases …

Scene classification is a highly useful task in Remote Sensing (RS) applications. Many
efforts have been made to improve the accuracy of RS scene classification. Scene …

Abstract Knowledge distillation aims at transferring knowledge acquired in one model (a
teacher) to another model (a student) that is typically smaller. Previous approaches can be …

Label smoothing is an effective regularization tool for deep neural networks (DNNs), which
generates soft labels by applying a weighted average between the uniform distribution and …

In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …