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Papers/YOLACT: Real-time Instance Segmentation

YOLACT: Real-time Instance Segmentation

Daniel Bolya, Chong Zhou, Fanyi Xiao, Yong Jae Lee

2019-04-04ICCV 2019 10Real-time Instance SegmentationSegmentationSemantic SegmentationTranslationInstance Segmentation
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Abstract

We present a simple, fully-convolutional model for real-time instance segmentation that achieves 29.8 mAP on MS COCO at 33.5 fps evaluated on a single Titan Xp, which is significantly faster than any previous competitive approach. Moreover, we obtain this result after training on only one GPU. We accomplish this by breaking instance segmentation into two parallel subtasks: (1) generating a set of prototype masks and (2) predicting per-instance mask coefficients. Then we produce instance masks by linearly combining the prototypes with the mask coefficients. We find that because this process doesn't depend on repooling, this approach produces very high-quality masks and exhibits temporal stability for free. Furthermore, we analyze the emergent behavior of our prototypes and show they learn to localize instances on their own in a translation variant manner, despite being fully-convolutional. Finally, we also propose Fast NMS, a drop-in 12 ms faster replacement for standard NMS that only has a marginal performance penalty.

Results

TaskDatasetMetricValueModel
Instance SegmentationCOCO minivalmask AP29.9YOLACT-550 (ResNet-50)
Instance SegmentationMSCOCOAP5046.6YOLACT-550 (ResNet-101-FPN)
Instance SegmentationMSCOCOAP7529.2YOLACT-550 (ResNet-101-FPN)
Instance SegmentationMSCOCOAPL44.8YOLACT-550 (ResNet-101-FPN)
Instance SegmentationMSCOCOAPM29.3YOLACT-550 (ResNet-101-FPN)
Instance SegmentationMSCOCOAPS9.2YOLACT-550 (ResNet-101-FPN)
Instance SegmentationMSCOCOmask AP28.2YOLACT-550 (ResNet-101-FPN)
Instance SegmentationMSCOCOAP5042YOLACT
Instance SegmentationMSCOCOAP7525.4YOLACT
Instance SegmentationMSCOCOAPL45YOLACT
Instance SegmentationMSCOCOAPM25.3YOLACT
Instance SegmentationMSCOCOAPS5YOLACT
Instance SegmentationMSCOCOmask AP24.9YOLACT

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