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Single-stage Instance Segmentation
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DOI：10.1145/3387926.png)
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Abstract
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Albeit the highest accuracy of object detection is generally acquired by multi-stage detectors, like R-CNN and its extension approaches, the single-stage object detectors also achieve remarkable performance with faster execution and higher scalability. Inspired by this, we propose a single-stage framework to tackle the instance segmentation task. Building on a single-stage object detection network in hand, our model outputs the detected bounding box of each instance, the semantic segmentation result, and the pixel affinity simultaneously. After that, we generate the final instance masks via a fast post-processing method with the help of the three outputs above. As far as we know, it is the first attempt to segment instances in a single-stage pipeline on challenging datasets. Extensive experiments demonstrate the efficiency of our post-processing method, and the proposed framework obtains competitive results as a single-stage instance segmentation method. We achieve 32.5 box AP and 26.0 mask AP on the COCO validation set with 500 pixels input scale and 22.9 mask AP on the Cityscapes test set.
Keywords:
Instance segmentation
neural networks
single stage
graph merge
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