The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Instance-Level Segmentation for Autonomous Driving with Deep Densely Connected MRFs
Open accessAbstract
Our aim is to provide a pixel-wise instance-level labeling of a monocular image in the context of autonomous driving. We build on recent work [32] that trained a convolutional neural net to predict instance labeling in local image patches, extracted exhaustively in a stride from an image. A simple Markov random field model using several heuristics was then proposed in [32] to derive a globally consistent instance labeling of the image. In this paper, we formulate the global labeling problem with a novel densely connected Markov random field and show how to encode various intuitive potentials in a way that is amenable to efficient mean field inference [15]. Our potentials encode the compatibility between the global labeling and the patch-level predictions, contrast-sensitive smoothness as well as the fact that separate regions form different instances. Our experiments on the challenging KITTI benchmark [8] demonstrate that our method achieves a significant performance boost over the baseline [32].
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897
Self-citationSemantic 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
Parsing IKEA Objects: Fine Pose Estimation
Lim, J. J., Pirsiavash, H., & Torralba, A. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.372 · Cited by 249
Cited by
Path Aggregation Network for Instance Segmentation
Liu, S., Qi, L., Qin, H., Shi, J., & Jia, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00913 · Cited by 10,011
Hybrid Task Cascade for Instance Segmentation
Chen, K., Pang, J., Wang, J., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., Shi, J., Ouyang, W., et al. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00511 · Cited by 1,587
Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation
Cheng, B., Collins, M. D., Zhu, Y., Liu, T., Huang, T. S., Adam, H., & Chen, L.-C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01249 · Cited by 635
Meta R-CNN: Towards General Solver for Instance-Level Low-Shot Learning
Yan, X., Chen, Z., Xu, A., Wang, X., Liang, X., & Lin, L. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00967 · Cited by 616
MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features
Chen, L.-C., Hermans, A., Papandreou, G., Schroff, F., Wang, P., & Adam, H. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00422 · Cited by 373
Metadata
- Authors
- Zhang, Z., Fidler, S., & Urtasun, R.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 669-677
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2964211168
- Cited by
- 209
- References
- 4
- FWCI
- 8.58
- Type
- conference paper
- Language
- en
- Open access
- green
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- Zhang, Z., Fidler, S., & Urtasun, R. (2016). Instance-Level Segmentation for Autonomous Driving with Deep Densely Connected MRFs. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2016.79
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