A promising direction for recovering the lost information in low-resolut...
Humans are arguably one of the most important subjects in video streams,...
Generative adversarial networks (GANs), e.g., StyleGAN2, play a vital ro...
We introduce a neural network-based method to denoise pairs of images ta...
Image compositing is a task of combining regions from different images t...
We present a novel resizing module for neural networks: shape adaptor, a...
In deep CNN based models for semantic segmentation, high accuracy relies...
Current methods for active speak er detection focus on modeling short-te...
We present TDNet, a temporally distributed network designed for fast and...
We present TDNet, a temporally distributed network designed for fast and...
We present a method to improve the visual realism of low-quality, synthe...
Bursts of images exhibit significant self-similarity across both time an...
Large scale object detection datasets are constantly increasing their si...
We propose a novel multi-stream architecture and training methodology th...
We present the 2019 DAVIS Challenge on Video Object Segmentation, the th...
We present a fully data-driven method to compute depth from diverse mono...
Recent deep learning approaches to single image super-resolution have
ac...
Most recent semantic segmentation methods train deep convolutional neura...
Minimization of regularized losses is a principled approach to weak
supe...
We present the 2018 DAVIS Challenge on Video Object Segmentation, a publ...
We present the 2017 DAVIS Challenge, a public competition specifically
d...
Inspired by recent advances of deep learning in instance segmentation an...