It should be possible to do negative analysis of AI capabilities. But since there is almost no theory behind this stuff right now (just test sets and architectures targeting particular test sets), anyone can always claim that any negative analysis just didn't target the "right" architecture.
In any case, they directly exclude the case you mention.
Their paper was still unimaginative. Trying a handful of vanilla networks doesn’t prove anything.
The right framing would have been, “here’s an interesting case to work on” instead of pretending to have identified a basic limitation of neural networks. This is more like a research idea than research.
To be honest, I skipped it and went to the paper :) Apologies!
Faces have a limited set of common features that are readily separable, quantifiable, and comparable, which is why face recognition can be done rather well just using a SVM. When the set of common features falls to zero, as in this paper, a face-specific net architecture will not do one-shot recognition across independent faceted objects nearly as well.
Many networks can detect if two never-before-seen objects are the same or different. Simple example is Koch's Siamese Neural Networks[1]. More useful example is Facenet[2], which can tell if two faces are the same or different, even if never trained on these specific faces. Facenet learns an n-dimensional embedding and similarity is estimated by euclidean distance. These are cool papers by the way. The Omniglot dataset[3] used in Koch is a sort of an "inverse MNIST". Omniglot has 20 examples each of 1600 different characters, vs MNIST's 60,000 examples of 10 different characters.
[1] https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf
[2] https://arxiv.org/abs/1503.03832
[3] https://github.com/brendenlake/omniglot