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.

翻译：从意外事故中评估车辆损伤对汽车保险行业至关重要。然而，由于深度学习网络并非专为车辆损伤图像输入而设计，且其分割掩码仍较为粗糙，当前方法在实际应用中的精度仍显不足。本文提出MARS（基于顺序四叉树节点的掩码注意力精化方法）用于车辆损伤实例分割。MARS通过自注意力机制捕获顺序四叉树节点层与四叉树Transformer之间的全局依赖关系，从而重新校准通道权重并预测高精度实例掩码。大量实验表明，在泰国车辆损伤数据集上，基于R50-FPN骨干网络，MARS在maskAP指标上以+1.3的显著优势优于Mask R-CNN[9]、PointRend[13]和Mask Transfiner[12]等主流实例分割方法，基于R101-FPN骨干网络则提升+2.3。演示代码已开源至https://github.com/kaopanboonyuen/MARS。