103 citations · 309 across the 21 of their papers we have counts for
13 papers · 1 filter
Enhancing Perceptual Attributes with Bayesian Style Generation
Aliaksandr Siarohin, Gloria Zen, Nicu Sebe +1
Deep learning has brought an unprecedented progress in computer vision and significant advances have been made in predicting subjective properties inherent to visual data (e.g., me…
Animating Arbitrary Objects via Deep Motion Transfer
Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov +2
This paper introduces a novel deep learning framework for image animation. Given an input image with a target object and a driving video sequence depicting a moving object, our fra…
Unsupervised Adversarial Depth Estimation using Cycled Generative Networks
Andrea Pilzer, Dan Xu, Mihai Marian Puscas +2
While recent deep monocular depth estimation approaches based on supervised regression have achieved remarkable performance, costly ground truth annotations are required during tra…
Kitting in the Wild through Online Domain Adaptation
Massimiliano Mancini, Hakan Karaoguz, Elisa Ricci +2
Technological developments call for increasing perception and action capabilities of robots. Among other skills, vision systems that can adapt to any possible change in the working…
Best sources forward: domain generalization through source-specific nets
Massimiliano Mancini, Samuel Rota Bulò, Barbara Caputo +1
A long standing problem in visual object categorization is the ability of algorithms to generalize across different testing conditions. The problem has been formalized as a covaria…
Adding New Tasks to a Single Network with Weight Transformations using Binary Masks
Massimiliano Mancini, Elisa Ricci, Barbara Caputo +1
Visual recognition algorithms are required today to exhibit adaptive abilities. Given a deep model trained on a specific, given task, it would be highly desirable to be able to ada…