论文标题
vec2instance：深度实例分割的参数化
Vec2Instance: Parameterization for Deep Instance Segmentation
论文作者
论文摘要
深度学习的当前进展导致人类级别的准确性在计算机视觉任务中，例如对象分类，本地化，语义细分和实例细分。在本文中，我们描述了一种新的深层卷积神经网络架构，称为Vec2Instance，例如分割。 VEC2Instance为实例参数化提供了一个框架，从而使卷积神经网络有效地估计了其质心周围实例的复杂形状。我们证明了提出的架构相对于具有广泛应用的卫星图像上的实例分割任务的可行性。此外，我们演示了新方法从卫星图像中提取构建足迹的有用性。我们方法的总像素准确性为89 \％，接近最先进的蒙版RCNN（91 \％）的准确性。 Vec2Instance是一种复杂实例分割管道的替代方法，提供简单性和直觉。根据本研究制定的代码可在vec2instance github存储库中获得https://github.com/lakmalnd/vec2instance
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
