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
arXiv Machine Learning
Jun 9

Energy-Regularized Spatial Masking: A Novel Approach to Enhancing Robustness and Interpretability in Vision Models

arXiv:2604. 06893v3 Announce Type: replace-cross Abstract: Deep convolutional neural networks achieve remarkable performance by exhaustively processing dense spatial feature maps, yet this brute-force strategy introduces significant computational redundancy and encourages reliance on spurious background correlations.

By Tom Devynck, Bilal Faye, Djamel Bouchaffra, Nadjib Lazaar, Hanane Azzag, Mustapha Lebbah