The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Simple Does It: Weakly Supervised Instance and Semantic Segmentation
Abstract
Semantic labelling and instance segmentation are two tasks that require particularly costly annotations. Starting from weak supervision in the form of bounding box detection annotations, we propose a new approach that does not require modification of the segmentation training procedure. We show that when carefully designing the input labels from given bounding boxes, even a single round of training is enough to improve over previously reported weakly supervised results. Overall, our weak supervision approach reaches ~95% of the quality of the fully supervised model, both for semantic labelling and instance segmentation.
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Cites
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392
Multi-scale context aggregation by dilated convolutions
Yu, F., & Koltun, V. (2015) · arXiv · doi:10.48550/arxiv.1511.07122 · Cited by 7,738
The Pascal Visual Object Classes Challenge: A Retrospective
Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-014-0733-5 · Cited by 7,139
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2014) · arXiv · doi:10.48550/arxiv.1412.7062 · Cited by 4,213
Conditional Random Fields as Recurrent Neural Networks
Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., & Torr, P. H. S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.179 · Cited by 2,386
Hypercolumns for object segmentation and fine-grained localization
Hariharan, B., Arbelaez, P., Girshick, R., & Malik, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298642 · Cited by 1,511
ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation
Lin, D., Dai, J., Jia, J., He, K., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.344 · Cited by 1,129
Cited by
Learning to Adapt Structured Output Space for Semantic Segmentation
Tsai, Y.-H., Hung, W.-C., Schulter, S., Sohn, K., Yang, M.-H., & Chandraker, M. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00780 · Cited by 1,430
A review of semantic segmentation using deep neural networks
Guo, Y., Liu, Y., Georgiou, T., & Lew, M. S. (2018) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-017-0141-z · Cited by 882
BoxInst: High-Performance Instance Segmentation with Box Annotations
Tian, Z., Shen, C., Wang, X., & Chen, H. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00540 · Cited by 341
Normalized Cut Loss for Weakly-Supervised CNN Segmentation
Tang, M., Djelouah, A., Perazzi, F., Boykov, Y., & Schroers, C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00195 · Cited by 336
Learning to Segment Every Thing
Hu, R., Dollar, P., He, K., Darrell, T., & Girshick, R. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00445 · Cited by 281
Metadata
- Authors
- Khoreva, A., Benenson, R., Hosang, J., Hein, M., & Schiele, B.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 1665-1674
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2552414813
- Cited by
- 764
- References
- 14
- FWCI
- 20.14
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Khoreva, A., Benenson, R., Hosang, J., Hein, M., & Schiele, B. (2017). Simple Does It: Weakly Supervised Instance and Semantic Segmentation. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2017.181
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