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ViewAL: Active Learning With Viewpoint Entropy for Semantic Segmentation
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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2 Google 7% randomly selected data 29.9% mIoU Segmentation Cross Entropy Loss 7% actively selected data 43.2% mIoU 100% data 45.6% mIoU Ground-Truth RGB Image floor wall objects High CE-Loss Low CE-Loss Figure 1 : ViewAL is an active learning method that significantly reduces labeling effort: with maximum performance attained by using 100% of the data (last column), ViewAL achieves 95% of this performance with only 7% of data of SceneNet-RGBD [28] . With the same data, the best state-of-the-art
doi:10.1109/cvpr42600.2020.00945
dblp:conf/cvpr/SiddiquiVN20
fatcat:svk3i5uztvexnjsdwqmisrqsom