transferring knowledge from natural photography to art objects has been previously addressed beyond the recent transfer from ImageNet to WikiArt [@Tan:16]; yet their solution is general as it is common to use ImageNet pre-trained CNNs on smaller databases. 3D object reconstruction can be augmented if information from old paintings is available [@Aubry2013]. Classifiers (deep CNNs) trained on real data are able to locate objects such as cars, cows and cathedrals [@Crowley2016] if the artistic rendering is not very abstract. The problem of detecting/recognizing objects in any type of data, regardless if it is real or artistic, was named cross-depiction by Hall et al. [@Hall2015]; however the problem is noted as being particular difficult and in the light of dedicated benchmarks [@Cai2015], the results show plenty of space for improvement.

A significant conclusion that arises is that all solutions that showed some degree of success did it for *older* artistic movements, where scene depiction was without particular abstraction. To our best knowledge there is no reported significant success for (more) modern art.

**Scene recognition in photographs.** Scene recognition in natural images is an intensively studied topic, but under the auspices of being significantly more difficult than object recognition or image classification 