  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 attacked the problem either by requiring intense user
interactions or by exploiting the ability of convolutional neural networks
(CNNs) in learning image level (context) features. However, obtaining human
hints is not always feasible and CNNs alone are not able to learn object-level
semantics unless multiple models pretrained with supervision are considered. In
this work, we propose a single network, named UCapsNet, that separate
image-level features obtained through convolutions and object-level features
captured by means of capsules. Then, by skip connections over different layers,
we enforce collaboration between such disentangling factors to produce high
quality and plausible image colourisation. We pose the problem as a
classification task that can be addressed by a fully self-supervised approach,
thus requires no human effort. Experimental results on three benchmark datasets
show that our approach outperforms existing methods on standard quality metrics
and achieves a state of the art performances on image colourisation. A large
scale user study shows that our method is preferred over existing solutions.
