Simple solutions are great and I find seam carving elegant, but maybe that's an application where machine learning can shine? As globally the task is defined a

Simple solutions are great and I find seam carving elegant, but maybe that's an application where machine learning can shine? As globally the task is defined as displacing pixels while minimizing a perceptual loss, it should be reasonably easy to express in a differentiable way. The benefits I see are higher quality semantics preservation, and potentially faster inference (one pass only). The recent development of transformer models might provide just the tool to tackle variable sizes efficiently, maybe I should give it a go Edit: if you're interested too and want to play on it together, shoot me a message :)