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 …

Semantic segmentation is the task of labeling every pixel in an image with a predefined
object category. It has numerous applications in scenarios where the detailed understanding …

Background In recent years quantitative analysis of root growth has become increasingly
important as a way to explore the influence of abiotic stress such as high temperature and …

The conditional random fields (CRFs) model plays an important role in the machine learning
field. Driven by the development of the artificial intelligence, the CRF models have enjoyed …

Abstract We introduce regularized Frank-Wolfe, a general and effective algorithm for
inference and learning of dense conditional random fields (CRFs). The algorithm optimizes …

Deep convolutional neural networks (DCNNs) have been driving significant advances in
semantic image segmentation due to their powerful feature representation for recognition …

Scene understanding is a significant research topic in computer vision, especially for robots
to understand their environment intelligently. Semantic scene segmentation can help robots …

Fully connected pairwise Conditional Random Fields (Full-CRF) with Gaussian edge
weights can achieve superior results compared to sparsely connected CRFs. However …

Are we using the right potential functions in the Conditional Random Field models that are
popular in the Vision community? Semantic segmentation and other pixel-level labelling …

Abstract Background and Objective Atrial septal defect (ASD) is a common congenital heart
disease. During embryonic development, abnormal atrial septal development leads to pores …