论文标题
用动态级别1卷积统一实例和全景分割
Unifying Instance and Panoptic Segmentation with Dynamic Rank-1 Convolutions
论文作者
论文摘要
最近，与两个阶段框架相比，完全跨跨跨跨跨阶段的一阶段网络表现出了出色的性能，例如分割，因为它们通常可以以更少的计算生成更高质量的掩码预测。此外，他们的简单设计为联合多任务学习开辟了新的机会。在本文中，我们证明了为语义分割添加单个分类层，完全跨跨实例的分割网络可以达到最新的全盘分割质量。我们的新型动态级别1卷积（DR1CONV）使它成为可能，这是一个新型的动态模块，可以有效地将高级上下文信息与低级详细特征合并，这对语义和实例分割都是有益的。重要的是，所提出的新方法称为DR1mask，可以通过添加单层执行泛滥分割。据我们所知，DR1mask是第一个泛型分割框架，它通过考虑疗效和效率来利用实例和语义分割的共享特征映射。我们的框架不仅要高得多 - 是以前最佳的两分支方法的速度两倍，而且统一框架为使用相同的上下文模块改善这两个任务的性能开辟了机会。作为副产品，在单独执行实例分割时，DR1mask比以前的最新实例分割网络BlendMask更快地速度10％，而MAP中的1点更准确。代码可在以下网址找到：https：//git.io/adelaidet
Recently, fully-convolutional one-stage networks have shown superior performance comparing to two-stage frameworks for instance segmentation as typically they can generate higher-quality mask predictions with less computation. In addition, their simple design opens up new opportunities for joint multi-task learning. In this paper, we demonstrate that adding a single classification layer for semantic segmentation, fully-convolutional instance segmentation networks can achieve state-of-the-art panoptic segmentation quality. This is made possible by our novel dynamic rank-1 convolution (DR1Conv), a novel dynamic module that can efficiently merge high-level context information with low-level detailed features which is beneficial for both semantic and instance segmentation. Importantly, the proposed new method, termed DR1Mask, can perform panoptic segmentation by adding a single layer. To our knowledge, DR1Mask is the first panoptic segmentation framework that exploits a shared feature map for both instance and semantic segmentation by considering both efficacy and efficiency. Not only our framework is much more efficient -- twice as fast as previous best two-branch approaches, but also the unified framework opens up opportunities for using the same context module to improve the performance for both tasks. As a byproduct, when performing instance segmentation alone, DR1Mask is 10% faster and 1 point in mAP more accurate than previous state-of-the-art instance segmentation network BlendMask. Code is available at: https://git.io/AdelaiDet
