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> Also I'd like to thank Ferenc Huszar for highlighting some drawbacks of these kinds of models.

As someone who understands ML concepts but hasn't used ML in practice, can someone elaborate on these drawbacks compared to using a traditional neural network?

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In a traditional CNN you use the same convolution kernel across an entire image. Here you do the same but you convolve the features of a person (pixel) with the feature of its neighbors, but those change depending on the network. Drawbacks are: I) you add one new person to the network, the whole thing has to be retrained; ii) convolutions are more complex and expensive

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