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- W3206887795 abstract "This paper presents Mask-aware Intersection-over-Union (maIoU) for assigning anchor boxes as positives and negatives during training of instance segmentation methods. Unlike conventional IoU or its variants, which only considers the proximity of two boxes; maIoU consistently measures the proximity of an anchor box with not only a ground truth box but also its associated ground truth mask. Thus, additionally considering the mask, which, in fact, represents the shape of the object, maIoU enables a more accurate supervision during training. We present the effectiveness of maIoU on a state-of-the-art (SOTA) assigner, ATSS, by replacing IoU operation by our maIoU and training YOLACT, a SOTA real-time instance segmentation method. Using ATSS with maIoU consistently outperforms (i) ATSS with IoU by $sim 1$ mask AP, (ii) baseline YOLACT with fixed IoU threshold assigner by $sim 2$ mask AP over different image sizes and (iii) decreases the inference time by $25 %$ owing to using less anchors. Then, exploiting this efficiency, we devise maYOLACT, a faster and $+6$ AP more accurate detector than YOLACT. Our best model achieves $37.7$ mask AP at $25$ fps on COCO test-dev establishing a new state-of-the-art for real-time instance segmentation. Code is available at this https URL" @default.
- W3206887795 created "2021-10-25" @default.
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- W3206887795 date "2021-10-19" @default.
- W3206887795 modified "2023-09-27" @default.
- W3206887795 title "Mask-aware IoU for Anchor Assignment in Real-time Instance Segmentation" @default.
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