The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation
Open accessAbstract
We introduce a new loss function for the weakly-supervised training of semantic image segmentation models based on three guiding principles: to seed with weak localization cues, to expand objects based on the information about which classes can occur in an image, and to constrain the segmentations to coincide with object boundaries. We show experimentally that training a deep convolutional neural network using the proposed loss function leads to substantially better segmentations than previous state-of-the-art methods on the challenging PASCAL VOC 2012 dataset. We furthermore give insight into the working mechanism of our method by a detailed experimental study that illustrates how the segmentation quality is affected by each term of the proposed loss function as well as their combinations.
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Cites
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The pascal visual object classes (VOC) challenge
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Research works printed but not linked: 6
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- Kolesnikov, A., Lampert, C.H.: Seed, expand and constrain: Three principles for weakly-supervised image segmentation. arXiv preprint arXiv:1603.06098 (2016). http://arxiv.org/abs/1603.06098 · arXiv
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Cited by
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01228-7 · Cited by 6,909
Deep visual domain adaptation: A survey
Wang, M., & Deng, W. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.05.083 · Cited by 2,209
Ahn, J., & Kwak, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00523 · Cited by 872
Self-Supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation
Wang, Y., Zhang, J., Kan, M., Shan, S., & Chen, X. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01229 · Cited by 770
Causal Intervention for Weakly-Supervised Semantic Segmentation
Zhang, D., Zhang, H., Tang, J., Hua, X., & Sun, Q. (2020) · arXiv · doi:10.48550/arxiv.2009.12547 · Cited by 353
Metadata
- Authors
- Kolesnikov, A., & Lampert, C. H.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 695-711
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2306289963
- Cited by
- 719
- References
- 48
- FWCI
- 20.67
- Type
- conference paper
- Language
- en
- Open access
- green
- Licence
- www.springer.com/tdm (tdm)
- www.springer.com/tdm (vor)
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