CASNet：图像实例和全景分割的公共属性支持网络
CASNet: Common Attribute Support Network for image instance and panoptic segmentation
摘要：
实例分割和全景分割是近年来研究的热点。与基于限界框的目标检测和语义分割相比，实例分割可以提供更多的像素级分析结果。针对同一实例中的像素点具有当前实例的一个或多个公共属性的特点，提出了一种单级实例分割网络--公共属性支持网络（CASNet），通过对公共属性的预测和聚类实现实例分割。CASNet采用全卷积的方式设计，可以实现端到端的训练和推理。CASNet能够在预测实例时避免当前大多数实例分割算法存在的重叠和空洞问题。此外，它可以很容易地扩展到全景分割，通过较小的修改与很少的计算开销。CASNet从查找像素类ID到通过对公共属性的操作获得类ID和实例ID，在语义分割和实例分割之间架起了一座桥梁。通过实例实验和全景分割，CASNet在Cityscapes验证数据集上采用联合训练的方法得到mAP 32.8%，PQ 59.0%，采用分离训练的方法得到mAP 36.3%，PQ 66.1%.对于全景分割，CASNet在Cityscapes验证数据集上获得了最先进的性能。
英文摘要：
Instance segmentation and panoptic segmentation is being paid more and more attention in recent years. In comparison with bounding box based object detection and semantic segmentation, instance segmentation can provide more analytical results at pixel level. Given the insight that pixels belonging to one instance have one or more common attributes of current instance, we bring up an one-stage instance segmentation network named Common Attribute Support Network (CASNet), which realizes instance segmentation by predicting and clustering common attributes. CASNet is designed in the manner of fully convolutional and can implement training and inference from end to end. And CASNet manages predicting the instance without overlaps and holes, which problem exists in most of current instance segmentation algorithms. Furthermore, it can be easily extended to panoptic segmentation through minor modifications with little computation overhead. CASNet builds a bridge between semantic and instance segmentation from finding pixel class ID to obtaining class and instance ID by operations on common attribute. Through experiment for instance and panoptic segmentation, CASNet gets mAP 32.8% and PQ 59.0% on Cityscapes validation dataset by joint training, and mAP 36.3% and PQ 66.1% by separated training mode. For panoptic segmentation, CASNet gets state-of-the-art performance on the Cityscapes validation dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
