Towards Light-Weight Transformer-Based Quality Assessment Metric for Augmented Reality

A lightweight transformer-based approach to perceptual quality assessment in augmented reality, designed to cut computational cost while preserving prediction performance on subjective AR quality datasets.

This is the conference paper that TransformAR extends: it establishes that a compact transformer, trained with the right supervision, is sufficient to model the interaction between the augmented foreground and the captured background.