The DOI matches its Crossref record; 105 of the 105 references it cites were found in a registry or an index.
Review the state-of-the-art technologies of semantic segmentation based on deep learning
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Citations
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380
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
Cited by
Activation functions in deep learning: a comprehensive survey and benchmark
Dubey, S. R., Singh, S. K., & Chaudhuri, B. B. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.06.111 · Cited by 1,259
Ragazou, K., Garefalakis, A., Zafeiriou, E., & Passas, I. (2022) · Energies · doi:10.3390/en15093113 · Cited by 125
Deep Learning Approaches for Enhanced Lower-Limb Exoskeleton Control: A Review
Belal, M., Alsheikh, N., Aljarah, A., & Hussain, I. (2024) · IEEE Access · doi:10.1109/access.2024.3414175 · Cited by 42
Conformal Semantic Image Segmentation: Post-hoc Quantification of Predictive Uncertainty
Mossina, L., Dalmau, J., & Andéol, L. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00361 · Cited by 18
Metadata
- Authors
- Mo, Y., Wu, Y., Yang, X., Liu, F., & Liao, Y.
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2022-07
- Volume
- 493
- Pages
- 626-646
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W4206573688
- Cited by
- 632
- References
- 105
- FWCI
- 46.22
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Review the state-of-the-art technologies of semantic segmentation based on deep learning. https://app.cochat.ai/papers/10.1016/j.neucom.2022.01.005
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- Mo, Y., Wu, Y., Yang, X., Liu, F., & Liao, Y. (2022). Review the state-of-the-art technologies of semantic segmentation based on deep learning. Neurocomputing. https://doi.org/10.1016/j.neucom.2022.01.005
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
