The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
Normalized Cut Loss for Weakly-Supervised CNN Segmentation
Abstract
Most recent semantic segmentation methods train deep convolutional neural networks with fully annotated masks requiring pixel-accuracy for good quality training. Common weakly-supervised approaches generate full masks from partial input (e.g. scribbles or seeds) using standard interactive segmentation methods as preprocessing. But, errors in such masks result in poorer training since standard loss functions (e.g. cross-entropy) do not distinguish seeds from potentially mislabeled other pixels. Inspired by the general ideas in semi-supervised learning, we address these problems via a new principled loss function evaluating network output with criteria standard in "shallow" segmentation, e.g. normalized cut. Unlike prior work, the cross entropy part of our loss evaluates only seeds where labels are known while normalized cut softly evaluates consistency of all pixels. We focus on normalized cut loss where dense Gaussian kernel is efficiently implemented in linear time by fast Bilateral filtering. Our normalized cut loss approach to segmentation brings the quality of weakly-supervised training significantly closer to fully supervised methods.
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Chen, X., Williams, B. M., Vallabhaneni, S. R., Czanner, G., Williams, R., & Zheng, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01190 · Cited by 334
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Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals
Van Gansbeke, W., Vandenhende, S., Georgoulis, S., & Van Gool, L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00990 · Cited by 231
Interactive Object Segmentation With Inside-Outside Guidance
Zhang, S., Liew, J. H., Wei, Y., Wei, S., & Zhao, Y. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01225 · Cited by 142
BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotations
Li, D., Ling, H., Kim, S. W., Kreis, K., Fidler, S., & Torralba, A. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.02064 · Cited by 83
Metadata
- Authors
- Tang, M., Djelouah, A., Perazzi, F., Boykov, Y., & Schroers, C.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 1818-1827
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963198662
- Cited by
- 336
- References
- 57
- FWCI
- 10.15
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Tang, M., Djelouah, A., Perazzi, F., Boykov, Y., & Schroers, C. (2018). Normalized Cut Loss for Weakly-Supervised CNN Segmentation. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2018.00195
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