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On the Importance of Label Quality for Semantic Segmentation

Author(s)
Zlateski, Aleksandar; Jaroensri, Ronnachai; Sharma, Prafull; Durand, Frederic
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Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/
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Abstract
Convolutional networks (ConvNets) have become the dominant approach to semantic image segmentation. Producing accurate, pixel-level labels required for this task is a tedious and time consuming process; however, producing approximate, coarse labels could take only a fraction of the time and effort. We investigate the relationship between the quality of labels and the performance of ConvNets for semantic segmentation. We create a very large synthetic dataset with perfectly labeled street view scenes. From these perfect labels, we synthetically coarsen labels with different qualities and estimate human-hours required for producing them. We perform a series of experiments by training ConvNets with a varying number of training images and label quality. We found that the performance of ConvNets mostly depends on the time spent creating the training labels. That is, a larger coarsely-annotated dataset can yield the same performance as a smaller finely-annotated one. Furthermore, fine-tuning coarsely pre-trained ConvNets with few finely-annotated labels can yield comparable or superior performance to training it with a large amount of finely-annotated labels alone, at a fraction of the labeling cost. We demonstrate that our result is also valid for different network architectures, and various object classes in an urban scene.
Date issued
2018-12
URI
https://hdl.handle.net/1721.1/124403
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Journal
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Publisher
IEEE
Citation
Zlateski, Aleksandar et al. "On the Importance of Label Quality for Semantic Segmentation." 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, IEEE, December 2018. © 2018 IEEE.
Version: Author's final manuscript
ISBN
9781538664209

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