Weakly- and Semi-supervised Panoptic Segmentation
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The WoodScape semantic segmentation dataset provides pixel-wise labels for 40 object categories, compared to Cityscapes' 30.
“Our semantic segmentation dataset provides pixel-wise labels for 40 object categories, comparatively Cityscapes dataset [10] provides 30 for example.”
From WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving (2019) · cites this paper as [10]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
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 · see citing works
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 · cited 1× · see citing works
“The Maximum-a-Posteriori (MAP) estimate of the CRF is the final labelling, and this is obtained by using mean-field inference, which is formulated as a differentiable, recurrent network [56, 57].”
MethodSelf-citationInstance-aware Semantic Segmentation via Multi-task Network Cascades
Dai, J., He, K., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.04412 · Cited by 1,273 · see citing works
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Kirillov, A., He, K., Girshick, R., Rother, C., & Dollár, P. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00963 · Cited by 1,659 · see citing works
Panoptic Feature Pyramid Networks
Kirillov, A., Girshick, R., He, K., & Dollár, P. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00656 · Cited by 1,460 · see citing works
Mixed supervision for surface-defect detection: From weakly to fully supervised learning
Božič, J., Tabernik, D., & Skočaj, D. (2021) · Computers in Industry · doi:10.1016/j.compind.2021.103459 · Cited by 377 · see citing works
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 · see citing works
WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving
Yogamani, S., Hughes, C., Horgan, J., Sistu, G., Chennupati, S., Uricar, M., Milz, S., Simon, M., Amende, K., Witt, C., et al. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00940 · Cited by 303 · see citing works
“The dataset also provides instance segmentation labels to explore panoptic segmentation models [34].”
MethodNaive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation
Chen, L.-C., Lopes, R. G., Cheng, B., Collins, M. D., Cubuk, E. D., Zoph, B., Adam, H., & Shlens, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58545-7_40 · Cited by 165 · see citing works
- Authors
- Li, Q., Arnab, A., & Torr, P. H. S.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Arnab, Anurag
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 106-124
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2886773299
- Cited by
- 171
- References
- 4
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 17 · 2023: 14 · 2022: 29 · 2021: 47 · 2020: 24 · 2019: 32 · 2018: 1
- FWCI
- 7.83
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Engineering and Physical Sciences Research Council (EP/N019474/1, EP/M013774/1, EP/I001107/2)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- panoptic segmentation, weakly supervised learning, semi-supervised learning, instance segmentation, semantic segmentation, bounding boxes, annotation quality, Pascal VOC, cityscape
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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