使用Polygon-RNN++对分割数据集进行高效交互式标注
Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++
摘要：
使用对象掩码手动标记数据集非常耗时。在这项工作中，我们遵循Polygon-RNN的思想，使用人机交互来生成对象的多边形注释。我们对模型进行了几项重要的改进：1）我们设计了一种新的CNN编码器架构，2）展示了如何使用强化学习有效地训练模型，3）使用图神经网络显着提高输出分辨率，使模型能够准确地注释图像中的高分辨率对象。对Cityscapes数据集的广泛评估表明，我们的模型（我们称之为Polygon-RNN++）在自动（平均IoU的10%绝对和16%相对改善）和交互方式（需要注释者减少50%的点击）方面都显着优于原始模型。我们进一步分析了跨域场景，其中我们的模型在一个数据集上进行训练，并在来自不同领域的数据集上开箱即用。结果表明，Polygon-RNN++具有强大的泛化能力，与现有的像素方法相比取得了显着的改进。使用简单的在线微调，我们进一步实现了新数据集的注释时间的大幅减少，向实践中使用的交互式注释工具迈进了一步。
英文摘要：
Manually labeling datasets with object masks is extremely time consuming. In this work, we follow the idea of Polygon-RNN to produce polygonal annotations of objects interactively using humans-in-the-loop. We introduce several important improvements to the model: 1) we design a new CNN encoder architecture, 2) show how to effectively train the model with Reinforcement Learning, and 3) significantly increase the output resolution using a Graph Neural Network, allowing the model to accurately annotate high-resolution objects in images. Extensive evaluation on the Cityscapes dataset shows that our model, which we refer to as Polygon-RNN++, significantly outperforms the original model in both automatic (10% absolute and 16% relative improvement in mean IoU) and interactive modes (requiring 50% fewer clicks by annotators). We further analyze the cross-domain scenario in which our model is trained on one dataset, and used out of the box on datasets from varying domains. The results show that Polygon-RNN++ exhibits powerful generalization capabilities, achieving significant improvements over existing pixel-wise methods. Using simple online fine-tuning we further achieve a high reduction in annotation time for new datasets, moving a step closer towards an interactive annotation tool to be used in practice.
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