Mask-aware IoU for Anchor Assignment in Real-time Instance Segmentation

Date

2021-11-29

Author

ÖKSÜZ, KEMAL
ÇAM, BARIŞ CAN
Kahraman, Fehmi
Baltacı, Zeynep Sonat
Kalkan, Sinan
Akbaş, Emre

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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 ~1 mask AP, (ii) baseline YOLACT with fixed IoU threshold assigner by ~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

URI

https://bmvc2021-virtualconference.com/conference/papers/paper_0031.html
https://hdl.handle.net/11511/96015

Conference Name

The 32nd British Machine Vision Conference

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Department of Computer Engineering, Conference / Seminar

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Citation Formats

K. ÖKSÜZ, B. C. ÇAM, F. Kahraman, Z. S. Baltacı, S. Kalkan, and E. Akbaş, “Mask-aware IoU for Anchor Assignment in Real-time Instance Segmentation,” presented at the The 32nd British Machine Vision Conference, İngiltere, 2021, Accessed: 00, 2022. [Online]. Available: https://bmvc2021-virtualconference.com/conference/papers/paper_0031.html.