I've been doing some research into computer vision lately and constantly come across the ability of residual networks to improve performance. Intuitively I think I grasp them, however, I struggle to understand why they are not more prolific in other non-image based fields. I'm currently specialized in NLP and I rarely see them.
My question is, what non-image use-cases have you seen residual networks succeed? And why do you think they did?