论文标题
联合对象轮廓点和语义，例如分段
Joint Object Contour Points and Semantics for Instance Segmentation
论文作者
论文摘要
对象轮廓的属性具有极大的意义，例如分割任务。但是，当前大多数流行的深度神经网络都没有太多关注对象边缘信息。在制作实例分割数据集时，受到人类注释过程的启发，在本文中，我们提出了遮罩点R-CNN，旨在促进神经网络对对象边界的关注。具体而言，我们将原始的人类关键点检测任务扩展到任何对象的轮廓点检测。基于此类比，我们提出了掩盖R-CNN的轮廓点检测辅助任务，该任务可以通过有效地使用功能融合策略和多任务关节训练来提高不同任务之间的梯度流。结果，该模型将对对象的边缘更加敏感，并可以捕获更多的几何特征。从数量上讲，实验结果表明，我们的方法在CityScapes数据集上优于3.8％的香草蒙版R-CNN，而COCO数据集则优于0.8 \％。
The attributes of object contours has great significance for instance segmentation task. However, most of the current popular deep neural networks do not pay much attention to the object edge information. Inspired by the human annotation process when making instance segmentation datasets, in this paper, we propose Mask Point R-CNN aiming at promoting the neural network's attention to the object boundary. Specifically, we innovatively extend the original human keypoint detection task to the contour point detection of any object. Based on this analogy, we present an contour point detection auxiliary task to Mask R-CNN, which can boost the gradient flow between different tasks by effectively using feature fusion strategies and multi-task joint training. As a consequence, the model will be more sensitive to the edges of the object and can capture more geometric features. Quantitatively, the experimental results show that our approach outperforms vanilla Mask R-CNN by 3.8\% on Cityscapes dataset and 0.8\% on COCO dataset.
