The DOI matches its Crossref record; 189 of the 189 references it cites were found in a registry or an index.
Deep learning for monocular depth estimation: A review
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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
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
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
ImageNet classification with deep convolutional neural networks
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Exploiting Generative AI to Scale up Intelligent Tutoring Systems
Jakubův, J., Chvalovský, K., Goertzel, Z., Kaliszyk, C., Olšák, M., Piotrowski, B., Schulz, S., Suda, M., & Urban, J. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,658
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
Densely Connected Convolutional Networks
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Going deeper with convolutions
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Microsoft COCO: Common Objects in Context
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A review on the attention mechanism of deep learning
Niu, Z., Zhong, G., & Yu, H. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.03.091 · Cited by 3,379
Self-citationFederated learning on non-IID data: A survey
Zhu, H., Xu, J., Liu, S., & Jin, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.07.098 · Cited by 1,062
A Comprehensive Survey of Visual SLAM Algorithms
Macario Barros, A., Michel, M., Moline, Y., Corre, G., & Carrel, F. (2022) · Robotics · doi:10.3390/robotics11010024 · Cited by 429
Deep multi-view learning methods: A review
Yan, X., Hu, S., Mao, Y., Ye, Y., & Yu, H. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.03.090 · Cited by 322
Self-citationA comprehensive survey on robust image watermarking
Wan, W., Wang, J., Zhang, Y., Li, J., Yu, H., & Sun, J. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.02.083 · Cited by 264
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Metadata
- Authors
- Ming, Y., Meng, X., Fan, C., & Yu, H.
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2021-05
- Volume
- 438
- Pages
- 14-33
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W3118635606
- Cited by
- 454
- References
- 189
- FWCI
- 25.31
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Deep learning for monocular depth estimation: A review. https://app.cochat.ai/papers/10.1016/j.neucom.2020.12.089
- The paper
- Ming, Y., Meng, X., Fan, C., & Yu, H. (2021). Deep learning for monocular depth estimation: A review. Neurocomputing. https://doi.org/10.1016/j.neucom.2020.12.089
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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.
