标题： Vec2Instance：深度实例分割的参数化方法 显示英文标题

标题： Vec2Instance: Parameterization for Deep Instance Segmentation

摘要： 当前深度学习的进展正在使计算机视觉任务中的人类水平准确率成为可能，例如物体分类、定位、语义分割和实例分割。 在本文中，我们描述了一种新的深度卷积神经网络架构，称为Vec2Instance，用于实例分割。 Vec2Instance提供了一个实例参数化的框架，使卷积神经网络能够高效地估计其质心周围的实例复杂形状。 我们通过在卫星图像上的实例分割任务展示了所提出架构的可行性，这些图像有广泛的应用。 此外，我们展示了新方法在从卫星图像中提取建筑物轮廓方面的有用性。 我们方法的总体像素级准确率为89%，接近最先进的Mask RCNN（91%）的准确率。 Vec2Instance是一种复杂的实例分割流程的替代方法，提供了简单性和直观性。 本研究开发的代码可在Vec2Instance GitHub仓库中获得，https://github.com/lakmalnd/Vec2Instance 显示英文摘要

摘要： Current advances in deep learning is leading to human-level accuracy in computer vision tasks such as object classification, localization, semantic segmentation, and instance segmentation. In this paper, we describe a new deep convolutional neural network architecture called Vec2Instance for instance segmentation. Vec2Instance provides a framework for parametrization of instances, allowing convolutional neural networks to efficiently estimate the complex shapes of instances around their centroids. We demonstrate the feasibility of the proposed architecture with respect to instance segmentation tasks on satellite images, which have a wide range of applications. Moreover, we demonstrate the usefulness of the new method for extracting building foot-prints from satellite images. Total pixel-wise accuracy of our approach is 89\%, near the accuracy of the state-of-the-art Mask RCNN (91\%). Vec2Instance is an alternative approach to complex instance segmentation pipelines, offering simplicity and intuitiveness. The code developed under this study is available in the Vec2Instance GitHub repository, https://github.com/lakmalnd/Vec2Instance