WM-MoE: Weather-aware Multi-scale Mixture-of-Experts for Blind Adverse Weather Removal

arXiv:2303.13739v2 Announce Type: replace
Abstract: Adverse weather removal tasks like deraining, desnowing, and dehazing are usually treated as separate tasks. However, in practical autonomous driving scenarios, the type, intensity,and mixing degree of weather are unknown, so handling each task separately cannot deal with the complex practical scenarios. In this paper, we study the blind adverse weather removal problem. Mixture-of-Experts (MoE) is a popular model that adopts a learnable gate to route the input to different expert networks. The principle of …

abstract arxiv autonomous autonomous driving blind cs.cv deal driving experts however intensity moe paper practical scale tasks type weather