Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN

Manually labeling datasets with object masks is extremely time consuming. In this work, we follow the idea of Polygon-RNN [4] 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 a...

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Veröffentlicht in:2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition S. 859 - 868
Hauptverfasser: Acuna, David, Ling, Huan, Kar, Amlan, Fidler, Sanja
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2018
Schlagworte:
Computer architecture
Decoding
Labeling
Neural networks
Predictive models
Training
ISSN:1063-6919
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Zusammenfassung:Manually labeling datasets with object masks is extremely time consuming. In this work, we follow the idea of Polygon-RNN [4] 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 [8] 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.
ISSN:1063-6919
DOI:10.1109/CVPR.2018.00096