Self-Calibrating Scene Understanding Based on Motifnet

We propose a self-calibrating approach to scene understanding that leverages Motifnet to jointly reason about object relationships and spatial context. The method enables adaptive calibration of scene graph generation without requiring explicit calibration supervision, improving relational inference in complex visual scenes. Experiments demonstrate that the self-calibration mechanism leads to more coherent and accurate scene representations across diverse visual environments.