However, my point was that most of the algorithms used on that link (ANN, SVM, etc) had similar expressive power (VC dimension) and had been proved to have similar performance between them in object recognition.

People normally take advantage on their specific properties rather than paying too much attention how well the algorithm would perform (since either SVM and ANN are expected to perform reasonably well). I still maintain my opinion that any difference in classification performance is more likely to be related to how the team managed the data instead of the chosen algorithm.

Deep convolutional learning is the difference here and indeed seems to be an interesting architecture which the current state of the art only support ANN. But that doesn't mean that somebody wouldn't come up with a strategy for deep learning on SVM or another classification technique in the future.