  Evaluating car damages from misfortune is critical to the car insurance
industry. However, the accuracy is still insufficient for real-world
applications since the deep learning network is not designed for car damage
images as inputs, and its segmented masks are still very coarse. This paper
presents MARS (Mask Attention Refinement with Sequential quadtree nodes) for
car damage instance segmentation. Our MARS represents self-attention mechanisms
to draw global dependencies between the sequential quadtree nodes layer and
quadtree transformer to recalibrate channel weights and predict highly accurate
instance masks. Our extensive experiments demonstrate that MARS outperforms
state-of-the-art (SOTA) instance segmentation methods on three popular
benchmarks such as Mask R-CNN [9], PointRend [13], and Mask Transfiner [12], by
a large margin of +1.3 maskAP-based R50-FPN backbone and +2.3 maskAP-based
R101-FPN backbone on Thai car-damage dataset. Our demos are available at
https://github.com/kaopanboonyuen/MARS.
