论文标题
PANONET：通过嵌入位置敏感的特征嵌入实时的全景进行分割
PanoNet: Real-time Panoptic Segmentation through Position-Sensitive Feature Embedding
论文作者
论文摘要
我们提出了一个简单，快速且灵活的框架，以同时生成语义和实例掩码，以进行泛滥分割。我们的方法称为Panonet，结合了一种干净自然的结构设计，该设计将问题纯粹作为分段任务解决，而无需耗时的检测过程。我们还通过计算对象的外观及其空间位置来介绍对位置敏感的嵌入。总体而言，全景可以实时产生高分辨率CityScapes图像的高泛质量结果，比所有其他具有可比性能的方法都要快得多。我们的方法很好地满足了许多应用程序（例如自动驾驶和增强现实）的实际速度和内存要求。
We propose a simple, fast, and flexible framework to generate simultaneously semantic and instance masks for panoptic segmentation. Our method, called PanoNet, incorporates a clean and natural structure design that tackles the problem purely as a segmentation task without the time-consuming detection process. We also introduce position-sensitive embedding for instance grouping by accounting for both object's appearance and its spatial location. Overall, PanoNet yields high panoptic quality results of high-resolution Cityscapes images in real-time, significantly faster than all other methods with comparable performance. Our approach well satisfies the practical speed and memory requirement for many applications like autonomous driving and augmented reality.
