AI RESEARCH
i-WiViG: Interpretable Window Vision GNN
arXiv CS.CV
•
ArXi:2503.08321v2 Announce Type: replace Vision graph neural networks have emerged as a popular approach for modeling the global and spatial context for image recognition. However, a significant drawback of these methods is that they do not offer an inherent interpretation of the relevant spatial interactions for their prediction. We address this problem by