论文标题
Banet：双向聚合网络，具有遮挡处理用于全景分割的
BANet: Bidirectional Aggregation Network with Occlusion Handling for Panoptic Segmentation
论文作者
论文摘要
PANOPTIC分割旨在对前景实例进行实例分割，并同时对背景内容进行语义分割。典型的自上而下管道集中在两个关键问题上：1）如何有效地对语义分割和实例分割之间的内在相互作用进行建模，以及2）如何正确处理宽带分割的闭塞。直观地，可以利用语义细分和实例分割之间的互补性来提高性能。此外，我们注意到使用检测/掩码分数不足以解决遮挡问题。在这些观察结果的驱动下，我们提出了一种基于双向学习管道的新型深层分割方案。此外，我们引入了一种插入式遮挡处理算法，以处理不同对象实例之间的遮挡。对可可泳道基准测试的实验结果验证了我们提出的方法的有效性。代码将很快在https://github.com/mooonside/banet上发布。
Panoptic segmentation aims to perform instance segmentation for foreground instances and semantic segmentation for background stuff simultaneously. The typical top-down pipeline concentrates on two key issues: 1) how to effectively model the intrinsic interaction between semantic segmentation and instance segmentation, and 2) how to properly handle occlusion for panoptic segmentation. Intuitively, the complementarity between semantic segmentation and instance segmentation can be leveraged to improve the performance. Besides, we notice that using detection/mask scores is insufficient for resolving the occlusion problem. Motivated by these observations, we propose a novel deep panoptic segmentation scheme based on a bidirectional learning pipeline. Moreover, we introduce a plug-and-play occlusion handling algorithm to deal with the occlusion between different object instances. The experimental results on COCO panoptic benchmark validate the effectiveness of our proposed method. Codes will be released soon at https://github.com/Mooonside/BANet.
