The general answer is yes - it's the "one shot learning" problem. But humans also have access to a large (if not very well defined) amount of data via background knowledge, which plausibly enables them to apply stronger, more effective priors to any one task.
So try it: Teach a computer about animals in general.
Then show it a single example of a new animal it has never seen, and see how well it would do at telling you if challenge images are or are not that animal.
It's not even going to be close to what a human would manage.
It's not because of the data, it's because a human understands what he's seeing.
Then you're comparing a machine that's never seen anything but animals against a human that has no separation between different kinds of data and can't start on a blank slate.
I may have all sorts of images in my mind, allowing me to make the distinction?
This doesn't sound too different from face recognition systems where you train a network to be able to tell if two faces are the same or different. I don't know what the state of the art in animal recognition is, though.
