论文标题
像素共识投票投票
Pixel Consensus Voting for Panoptic Segmentation
论文作者
论文摘要
我们的方法的核心，像素共识投票，是一个基于广义霍夫变换的细分框架。像素对包含实例质心的可能地区进行离散的，概率的投票。在投票热图中出现的检测到的峰值上，对反向投影进行了收集像素并产生实例口罩。与密集列出对象建议的滑动窗口检测器不同，我们的方法是由于像素票之间的共识而检测到实例。我们使用卷积神经网络的本机操作员实施投票汇总和反向投票。质心投票的离散化减少了实例分割对像素标签的训练，类似于FCN式的语义分割，从而导致了共同建模事物和东西的有效且统一的体系结构。我们证明了管道对可可和城市景观的有效性，并获得竞争结果。代码将是开源的。
The core of our approach, Pixel Consensus Voting, is a framework for instance segmentation based on the Generalized Hough transform. Pixels cast discretized, probabilistic votes for the likely regions that contain instance centroids. At the detected peaks that emerge in the voting heatmap, backprojection is applied to collect pixels and produce instance masks. Unlike a sliding window detector that densely enumerates object proposals, our method detects instances as a result of the consensus among pixel-wise votes. We implement vote aggregation and backprojection using native operators of a convolutional neural network. The discretization of centroid voting reduces the training of instance segmentation to pixel labeling, analogous and complementary to FCN-style semantic segmentation, leading to an efficient and unified architecture that jointly models things and stuff. We demonstrate the effectiveness of our pipeline on COCO and Cityscapes Panoptic Segmentation and obtain competitive results. Code will be open-sourced.
