That left me a bit disappointed about the current - I assume the tested detectors are at least somewhat state of the art - capabilities of neural network for ob

That left me a bit disappointed about the current - I assume the tested detectors are at least somewhat state of the art - capabilities of neural network for object identification. But thinking about it also makes it somewhat obvious that they are flawed in this way. Humans identify objects by looking at how different parts are geometrically located and connected, possibly in a hierarchical fashion, and what basic shapes, colors and textures those parts have. A sheep is a body, four legs, hoofs, a tail, a head with its characteristic shape, the ears, mouth, nose and all those come with characteristic textures and colors. And because there are so many features and their relations, it is quite hard to fool humans, you can hide or change quite a few of them. We also have a lot of background knowledge, a bright orange sheep might be unusual but we also have a pretty good idea of how hard it is to change the color of a sheep. I naively expected neural networks to also learn those features but there is just no pressure for them to do so. They mostly see common objects in common situations and there just looking for a patch of wool-colored, wool-textured fur might be enough to identify the presence of sheep correctly almost all the time. Or if sheep are mostly depicted in a characteristic environment it might be good enough to just identify landscape features and ignore the sheep altogether. I would guess that it is in general not really feasible to come up with enough contrived, adversarial examples to force neural networks to learn the important parts and relations of different objects just by starring at many images. I think one would have to hard-wire some knowledge about space, spatial relations, occlusion, shapes and the like into a system to really get it to learn what a sheep is in a similar way as humans do without heavily increasing the risk of over-fitting.