During the last decades, the research community of medical imaging has witnessed
continuous advances in image registration methods, which pushed the limits of the state-of …

Abstract Machine learning methods have been widely applied to big data analysis in
genomics and epigenomics research. Although accuracy and efficiency are common goals …

We present a general framework for exemplar-based image translation, which synthesizes a
photo-realistic image from the input in a distinct domain (eg, semantic segmentation mask …

This paper presents a simple yet effective approach to modeling space-time
correspondences in the context of video object segmentation. Unlike most existing …

As an essential problem in computer vision, salient object detection (SOD) has attracted an
increasing amount of research attention over the years. Recent advances in SOD are …

In this paper we challenge the common assumption that convolutional layers in modern
CNNs are translation invariant. We show that CNNs can and will exploit the absolute spatial …

We present a class of extremely efficient CNN models, MobileFaceNets, which use less than
1 million parameters and are specifically tailored for high-accuracy real-time face verification …

Image inpainting has made remarkable progress with recent advances in deep learning.
Popular networks mainly follow an encoder-decoder architecture (sometimes with skip …

Abstract Deep Neural Networks (DNNs) have substantially improved the state-of-the-art in
salient object detection. However, training DNNs requires costly pixel-level annotations. In …

Change detection (CD) in multitemporal images is an important application of remote
sensing. Recent technological evolution provided very high spatial resolution (VHR) …