arXiv Machine Learning
Aug 26

NAIMA: Semantics Aware RGB Guided Depth Super-Resolution

The paper introduces NAIMA, a guided depth super‑resolution framework that leverages global contextual semantic priors from pretrained vision transformer token embeddings. Its Guided Token Attention (GTA) module uses depth encodings as queries to attend over semantic tokens, with a zero‑initialized gate controlling the influence of semantic evidence. NAIMA achieves competitive in‑distribution performance while delivering superior cross‑dataset generalization without relying on decoded priors or auxiliary objectives.

By Tayyab Nasir, Daochang Liu, Ajmal Mian