The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Annotating Object Instances with a Polygon-RNN
Abstract
We propose an approach for semi-automatic annotation of object instances. While most current methods treat object segmentation as a pixel-labeling problem, we here cast it as a polygon prediction task, mimicking how most current datasets have been annotated. In particular, our approach takes as input an image crop and sequentially produces vertices of the polygon outlining the object. This allows a human annotator to interfere at any time and correct a vertex if needed, producing as accurate segmentation as desired by the annotator. We show that our approach speeds up the annotation process by a factor of 4.7 across all classes in Cityscapes, while achieving 78:4% agreement in IoU with original ground-truth, matching the typical agreement between human annotators. For cars, our speed-up factor is 7.3 for an agreement of 82:2%. We further show generalization capabilities of our approach to unseen datasets.
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Deep Residual Learning for Image Recognition
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Adam: A Method for Stochastic Optimization
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Self-citationThe Cityscapes Dataset for Semantic Urban Scene Understanding
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Multi-scale context aggregation by dilated convolutions
Yu, F., & Koltun, V. (2015) · arXiv · doi:10.48550/arxiv.1511.07122 · Cited by 7,738
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., & Woo, W.-C. (2015) · arXiv · doi:10.48550/arxiv.1506.04214 · Cited by 7,709
The Pascal Visual Object Classes Challenge: A Retrospective
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Self-citationThe Role of Context for Object Detection and Semantic Segmentation in the Wild
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Neural Motifs: Scene Graph Parsing with Global Context
Zellers, R., Yatskar, M., Thomson, S., & Choi, Y. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00611 · Cited by 1,052
(2018) · arXiv (Cornell University) · Cited by 679
MOTS: Multi-Object Tracking and Segmentation
Voigtlaender, P., Krause, M., Osep, A., Luiten, J., Sekar, B. B. G., Geiger, A., & Leibe, B. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00813 · Cited by 658
3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera
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Offboard 3D Object Detection from Point Cloud Sequences
Qi, C. R., Zhou, Y., Najibi, M., Sun, P., Vo, K., Deng, B., & Anguelov, D. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00607 · Cited by 176
Metadata
- Authors
- Castrejon, L., Kundu, K., Urtasun, R., & Fidler, S.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 4485-4493
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2609825896
- Cited by
- 355
- References
- 11
- FWCI
- 10.11
- Type
- conference paper
- Language
- en
- Open access
- closed
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