Fast and robust small target detection is one of the key technologies in the infrared (IR)
search and tracking systems. With the development of deep learning, there are many data …

Semantic segmentation is a challenging task in computer vision. In recent years, the
performance of semantic segmentation has been greatly improved by using deep learning …

The advent of big data and algorithmic developments in the field of machine learning (and
artificial intelligence, in general) have greatly impacted the entire spectrum of physical …

Deep learning approaches such as convolutional neural nets have consistently
outperformed previous methods on challenging tasks such as dense, semantic …

In many machine learning tasks it is desirable that a model's prediction transforms in an
equivariant way under transformations of its input. Convolutional neural networks (CNNs) …

We address the computational problem of novel human pose synthesis. Given an image of a
person and a desired pose, we produce a depiction of that person in that pose, retaining the …

Efficiently and accurately separating infrared (IR) small targets from complex backgrounds
presents a significant challenge. Numerous studies in the literature have proposed various …

Weakly-supervised learning based on, eg, partially labelled images or image-tags, is
currently attracting significant attention in CNN segmentation as it can mitigate the need for …

Indoor semantic segmentation has always been a difficult task in computer vision. In this
paper, we propose an RGB-D residual encoder-decoder architecture, named RedNet, for …

Class imbalance has emerged as one of the major challenges for medical image
segmentation. The model cascade (MC) strategy, a popular scheme, significantly alleviates …