论文标题
从颜色到点：重新访问语义细分监督
From colouring-in to pointillism: revisiting semantic segmentation supervision
论文作者
论文摘要
产生语义分割培训数据的主要范式依赖于训练集中每个图像的每个像素的密集标记，类似于颜色书籍。在图像，类和注释者的数量扩展时，这种方法变成了瓶颈。在这里，我们提出了一种语义分割注释的点方法，其中只有点是/否回答问题。我们探索了这种主动学习方法的设计替代方案，测量人类注释者在此任务上的速度和一致性，表明该策略使培训良好的分割模型，并且适合在测试时评估模型。作为我们方法可伸缩性的具体证明，我们在开放式图像数据集上收集并发布了22.6m点标签。我们的结果使从点的观点重新考虑注释，培训和评估的语义分割管道。
The prevailing paradigm for producing semantic segmentation training data relies on densely labelling each pixel of each image in the training set, akin to colouring-in books. This approach becomes a bottleneck when scaling up in the number of images, classes, and annotators. Here we propose instead a pointillist approach for semantic segmentation annotation, where only point-wise yes/no questions are answered. We explore design alternatives for such an active learning approach, measure the speed and consistency of human annotators on this task, show that this strategy enables training good segmentation models, and that it is suitable for evaluating models at test time. As concrete proof of the scalability of our method, we collected and released 22.6M point labels over 4,171 classes on the Open Images dataset. Our results enable to rethink the semantic segmentation pipeline of annotation, training, and evaluation from a pointillism point of view.
