[p1esk]

not quite solved is the right way to put it.

If you look at capsules papers, you will realize that convnets are not very good at recognizing transformations (e.g. 3d rotations) of the same object. That's probably why so many training examples are required to make them work well.

Also, if you look at errors made by state of the art models, some of them are obvious (to a human) objects, classified as something entirely different and unrelated. Which leads me to believe that object recognition is not completely solved until a model has some kind of common sense, either build in, or acquired during training.