Efficient Interactive Annotation of Segmentation Datasets With Polygon-RNN++

David Acuna, Huan Ling, Amlan Kar, Sanja Fidler; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 859-868

Abstract


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.

Related Material


[pdf] [Supp] [arXiv]
[bibtex]
@InProceedings{Acuna_2018_CVPR,
author = {Acuna, David and Ling, Huan and Kar, Amlan and Fidler, Sanja},
title = {Efficient Interactive Annotation of Segmentation Datasets With Polygon-RNN++},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}