Mask Encoding for Single Shot Instance Segmentation

• URL: http://arxiv.org/abs/2003.11712v2
• Date: Wed, 6 May 2020 12:44:26 GMT
• Title: Mask Encoding for Single Shot Instance Segmentation
• Authors: Rufeng Zhang, Zhi Tian, Chunhua Shen, Mingyu You, Youliang Yan
• Abstract summary: We propose a simple singleshot instance segmentation framework, termed mask encoding based instance segmentation (MEInst) Instead of predicting the two-dimensional mask directly, MEInst distills it into a compact and fixed-dimensional representation vector. We show that the much simpler and flexible one-stage instance segmentation method, can also achieve competitive performance.
• Score: 97.99956029224622
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: To date, instance segmentation is dominated by twostage methods, as pioneered by Mask R-CNN. In contrast, one-stage alternatives cannot compete with Mask R-CNN in mask AP, mainly due to the difficulty of compactly representing masks, making the design of one-stage methods very challenging. In this work, we propose a simple singleshot instance segmentation framework, termed mask encoding based instance segmentation (MEInst). Instead of predicting the two-dimensional mask directly, MEInst distills it into a compact and fixed-dimensional representation vector, which allows the instance segmentation task to be incorporated into one-stage bounding-box detectors and results in a simple yet efficient instance segmentation framework. The proposed one-stage MEInst achieves 36.4% in mask AP with single-model (ResNeXt-101-FPN backbone) and single-scale testing on the MS-COCO benchmark. We show that the much simpler and flexible one-stage instance segmentation method, can also achieve competitive performance. This framework can be easily adapted for other instance-level recognition tasks. Code is available at: https://git.io/AdelaiDet

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