This paper introduces Generalized Mask-aware Intersection-over-Union (GmaIoU) as a new measure for positive-negative assignment of anchor boxes during training of instance segmentation methods. Unlike conventional IoU measure or its variants, which only consider the proximity of anchor and ground-truth boxes; GmaIoU additionally takes into account the segmentation mask. This enables GmaIoU to provide more accurate supervision during training. We demonstrate the effectiveness of GmaIoU by replacing IoU with our GmaIoU in ATSS, a state-of-the-art (SOTA) assigner. Then, we train YOLACT, a real-time instance segmentation method, using our GmaIoU-based ATSS assigner. The resulting YOLACT based on the GmaIoU assigner outperforms (i) ATSS with IoU by $\sim 1.0-1.5$ mask AP, (ii) YOLACT with a fixed IoU threshold assigner by $\sim 1.5-2$ mask AP over different image sizes and (iii) decreases the inference time by $25 \%$ owing to using less anchors. Taking advantage of this efficiency, we further devise GmaYOLACT, a faster and $+7$ mask AP points more accurate detector than YOLACT. Our best model achieves $38.7$ mask AP at $26$ fps on COCO test-dev establishing a new state-of-the-art for real-time instance segmentation.

翻译：本文提出广义掩码感知交并比（Generalized Mask-aware Intersection-over-Union，GmaIoU）作为实例分割方法训练过程中锚点正负分配的新度量。与仅考虑锚点与真实边界框接近度的传统IoU度量或其变体不同，GmaIoU额外引入分割掩码信息，从而在训练过程中提供更精确的监督信号。我们通过将现有最先进分配器ATSS中的IoU替换为GmaIoU，验证了其有效性。进一步，基于GmaIoU的ATSS分配器训练实时实例分割方法YOLACT。实验表明：基于GmaIoU分配器的YOLACT相比（i）基于IoU的ATSS方法提升约1.0-1.5个掩码AP；（ii）基于固定IoU阈值的YOLACT在不同图像尺寸下提升约1.5-2个掩码AP；（iii）因减少锚点数量使推理时间降低25%。基于该效率优势，我们进一步设计出GmaYOLACT——相比YOLACT速度更快且AP提升+7个点的检测器。最佳模型在COCO test-dev数据集上以26 fps速度达到38.7掩码AP，刷新实时实例分割领域的最优性能。