The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Improving Semantic Segmentation via Video Propagation and Label Relaxation
Open accessAbstract
Semantic segmentation requires large amounts of pixel-wise annotations to learn accurate models. In this paper, we present a video prediction-based methodology to scale up training sets by synthesizing new training samples in order to improve the accuracy of semantic segmentation networks. We exploit video prediction models' ability to predict future frames in order to also predict future labels. A joint propagation strategy is also proposed to alleviate mis-alignments in synthesized samples. We demonstrate that training segmentation models on datasets augmented by the synthesized samples leads to significant improvements in accuracy. Furthermore, we introduce a novel boundary label relaxation technique that makes training robust to annotation noise and propagation artifacts along object boundaries. Our proposed methods achieve state-of-the-art mIoUs of 83.5% on Cityscapes and 82.9% on CamVid. Our single model, without model ensembles, achieves 72.8% mIoU on the KITTI semantic segmentation test set, which surpasses the winning entry of the ROB challenge 2018.
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Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_49 · Cited by 20,054
Self-citationSegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2644615 · Cited by 19,631
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
The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.350 · Cited by 12,433
Multi-scale context aggregation by dilated convolutions
Yu, F., & Koltun, V. (2015) · arXiv · doi:10.48550/arxiv.1511.07122 · Cited by 7,738
BiSeNet: Bilateral Segmentation Network for Real-Time Semantic Segmentation
Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., & Sang, N. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01261-8_20 · Cited by 2,968
Ros, G., Sellart, L., Materzynska, J., Vazquez, D., & Lopez, A. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.352 · Cited by 2,338
The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes
Neuhold, G., Ollmann, T., Bulo, S. R., & Kontschieder, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.534 · Cited by 1,434
Segmentation and Recognition Using Structure from Motion Point Clouds
Brostow, G. J., Shotton, J., Fauqueur, J., & Cipolla, R. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_5 · Cited by 1,050
Cited by
ResNeSt: Split-Attention Networks
Zhang, H., Wu, C., Zhang, Z., Zhu, Y., Lin, H., Zhang, Z., Sun, Y., He, T., Mueller, J., Manmatha, R., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00309 · Cited by 1,648
Self-citationObject-Contextual Representations for Semantic Segmentation
Yuan, Y., Chen, X., & Wang, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58539-6_11 · Cited by 1,285
PointPainting: Sequential Fusion for 3D Object Detection
Vora, S., Lang, A. H., Helou, B., & Beijbom, O. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00466 · Cited by 1,236
Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation
Wang, H., Zhu, Y., Green, B., Adam, H., Yuille, A., & Chen, L.-C. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58548-8_7 · Cited by 809
Self-citationPanoptic-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
Self-citationNaive-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
Accelerating Sparse Deep Neural Networks
Mishra, A., Latorre, J. A., Pool, J., Stosic, D., Stosic, D., Venkatesh, G., Yu, C., & Micikevicius, P. (2021) · arXiv · doi:10.48550/arxiv.2104.08378 · Cited by 97
“To study object detection and segmentation, we use networks from PyTorch Torchvision, Detectron2 [53], NVIDIA Deep Learning Examples for Tensor Cores [54], and NVIDIA ADLR [55] repositories.”
MethodHard pixel mining for depth privileged semantic segmentation
Gu, Z., Niu, L., Zhao, H., & Zhang, L. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3035231 · Cited by 41
Metadata
- Authors
- Zhu, Y., Sapra, K., Reda, F. A., Shih, K. J., Newsam, S., Tao, A., & Catanzaro, B.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 8848-8857
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2964254867
- Cited by
- 411
- References
- 10
- FWCI
- 22.18
- Type
- conference paper
- Language
- en
- Open access
- green
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