Brian McWilliams

TL;DR: This work presents a new benchmark dataset and evaluation methodology for the area of video object segmentation, named DAVIS (Densely Annotated VIdeo Segmentation), and provides a comprehensive analysis of several state-of-the-art segmentation approaches using three complementary metrics.

TL;DR: A novel, supervised learning approach that allows the filtering kernel to be more complex and general by leveraging a deep convolutional neural network (CNN) architecture and introduces a novel, kernel-prediction network which uses the CNN to estimate the local weighting kernels used to compute each denoised pixel from its neighbors.

TL;DR: ProGanSR as discussed by the authors proposes a progressive multi-scale GAN architecture that upsamples an image in intermediate steps, while the learning process is organized from easy to hard, as is done in curriculum learning.

TL;DR: It is shown that the correlation between gradients in standard feedforward networks decays exponentially with depth resulting in gradients that resemble white noise whereas, in contrast, thegradients in architectures with skip-connections are far more resistant to shattering, decaying sublinearly.

TL;DR: This paper showed that the correlation between gradients in standard feedforward networks decays exponentially with depth resulting in gradients that resemble white noise whereas, in contrast, the gradients of architectures with skip-connections are far more resistant to shattering, decaying sublinearly.