Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature.
Because hierarchy gives rise to unique properties and functions, many have sought …

Signal processing, communications, and control have traditionally relied on classical
statistical modeling techniques. Such model-based methods utilize mathematical …

Semantic segmentation is a challenging task in computer vision systems. A lot of methods
have been developed to tackle this problem ranging from autonomous vehicles, human …

Pathology image analysis is an essential procedure for clinical diagnosis of numerous
diseases. To boost the accuracy and objectivity of the diagnosis, nowadays, an increasing …

Aspect-based sentiment analysis produces a list of aspect terms and their corresponding
sentiments for a natural language sentence. This task is usually done in a pipeline manner …

Minimization of regularized losses is a principled approach to weak supervision well-
established in deep learning, in general. However, it is largely overlooked in semantic …

Abstract Deep Neural Networks (DNNs) have been demonstrated to perform exceptionally
well on most recognition tasks such as image classification and segmentation. However …

Exploiting long-range contextual information is key for pixel-wise prediction tasks such as
semantic segmentation. In contrast to previous work that uses multi-scale feature fusion or …

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 …

This paper addresses the problem of video object segmentation, where the initial object
mask is given in the first frame of an input video. We propose a novel spatio-temporal …