Transcription of YOLACT: Real-Time Instance Segmentation
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YOLACT: Real-Time Instance Segmentation
YOLACTReal-time Instance SegmentationDaniel BolyaChong ZhouFanyi XiaoYong Jae LeeUniversity of California, Davis{dbolya, cczhou, fyxiao, present a simple, fully-convolutional model for Real-Time Instance Segmentation that achieves mAP on MSCOCO at fps evaluated on a single Titan Xp, which issignificantly faster than any previous competitive , we obtain this result after training ononly oneGPU. We accomplish this by breaking Instance segmenta-tion into two parallel subtasks: (1) generating a set of pro-totype masks and (2) predicting per- Instance mask coeffi-cients. Then we produce Instance masks by linearly combin-ing the prototypes with the mask coefficients. We find thatbecause this process doesn t depend on repooling, this ap-proach produces very high-quality masks and exhibits tem-poral stability for free. Furthermore, we analyze the emer-gent behavior of our prototypes and show they learn to lo-calize instances on their own in a translation variant man-ner, despite being fully-convolutional.}
mentation, in part by drawing on powerful parallels from the well-established domain of object detection. State-of-the-art approaches to instance segmentation like Mask R-CNN [18] and FCIS [24] directly build off of advances in object detection like Faster R-CNN [37] and R-FCN [8]. Yet, these methods focus primarily on performance over
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