20 citations · 25 across the 7 of their papers we have counts for
10 papers
TUCaN: Progressively Teaching Colourisation to Capsules
Rita Pucci, Niki Martinel
Automatic image colourisation is the computer vision research path that studies how to colourise greyscale images (for restoration). Deep learning techniques improved image colouri…
Weakly-Supervised Domain Adaptation of Deep Regression Trackers via Reinforced Knowledge Distillation
Matteo Dunnhofer, Niki Martinel, Christian Micheloni
Deep regression trackers are among the fastest tracking algorithms available, and therefore suitable for real-time robotic applications. However, their accuracy is inadequate in ma…
Collaboration among Image and Object Level Features for Image Colourisation
Rita Pucci, Christian Micheloni, Niki Martinel
Image colourisation is an ill-posed problem, with multiple correct solutions which depend on the context and object instances present in the input datum. Previous approaches attack…
Is It a Plausible Colour? UCapsNet for Image Colourisation
Rita Pucci, Christian Micheloni, Gian Luca Foresti +1
Human beings can imagine the colours of a grayscale image with no particular effort thanks to their ability of semantic feature extraction. Can an autonomous system achieve that? C…
An Exploration of Target-Conditioned Segmentation Methods for Visual Object Trackers
Matteo Dunnhofer, Niki Martinel, Christian Micheloni
Visual object tracking is the problem of predicting a target object's state in a video. Generally, bounding-boxes have been used to represent states, and a surge of effort has been…
Tracking-by-Trackers with a Distilled and Reinforced Model
Matteo Dunnhofer, Niki Martinel, Christian Micheloni
Visual object tracking was generally tackled by reasoning independently on fast processing algorithms, accurate online adaptation methods, and fusion of trackers. In this paper, we…