Proposal-free instance segmentation methods mainly generate instance-agnostic semantic segmentation labels and instance-aware features to group pixels into different object instances. However, previous methods mostly employ separate modules for these two sub-tasks and require multiple passes for inference. In addition to the lack of efficiency, previous methods also failed to perform as well as proposal-based approaches. To this end, this work proposes a single-shot proposal-free instance segmentation method that requires only one single pass for prediction. Our method is based on learning an affinity pyramid, which computes the probability that two pixels belong to the same instance in a hierarchical manner. Moreover, incorporating with the learned affinity pyramid, a novel cascaded graph partition (CGP) module is presented to fuse the two predictions and segment instances efficiently. As an additional contribution, we conduct an experiment to demonstrate the benefits of proposalfree methods in capturing detailed structures from finely annotated training examples. Our approach is evaluated on the Cityscapes and COCO datasets and achieves state-of-the-art performance.

中文翻译：

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SSAP：使用 Affinity Pyramid 的 Single-Shot Instance Segmentation

无提议实例分割方法主要生成与实例无关的语义分割标签和实例感知特征，以将像素分组为不同的对象实例。然而，以前的方法大多为这两个子任务使用单独的模块，并且需要多次通过才能进行推理。除了缺乏效率之外，以前的方法也未能像基于提案的方法那样执行。为此，这项工作提出了一种单次无提议的实例分割方法，该方法只需要一次通过即可进行预测。我们的方法基于学习亲和金字塔，它以分层方式计算两个像素属于同一实例的概率。此外，结合学习到的亲和力金字塔，提出了一种新颖的级联图分区（CGP）模块来有效地融合两个预测和分段实例。作为额外的贡献，我们进行了一项实验，以证明无提议方法在从精细注释的训练示例中捕获详细结构方面的好处。我们的方法在 Cityscapes 和 COCO 数据集上进行了评估，并实现了最先进的性能。