The DOI matches its Crossref record; the references it cites are not checked yet.
Cross-scale fusion and domain adversarial network for generalizable rail surface defect segmentation on unseen datasets
Overview
Integrity signals
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Cites
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Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Labeling steel surface defect data is costly and labor intensive.
“the labeling of the data is also costly and labor intensive [4][5][6][7].”
From Steel Surface Defect Recognition: A Survey (2022) · cites this paper as [6]
In actual steel production, high-quality datasets for training machine learning algorithms are very difficult to obtain because defects are small probability events and obtaining many samples with various defect types is difficult.
“In the actual production environment, it is very difficult to obtain high quality datasets for training machine learning related algorithms because defects in steel production are originally small probability events, and it is very difficult to obtain many samples with various types of defects”
From Steel Surface Defect Recognition: A Survey (2022) · cites this paper as [6]
Citations
The citation lists haven’t been fetched yet
References 37 · Cited by 31
Research works printed but not linked: 5
- Tao, A., Sapra, K., & Catanzaro, B. (2020). Hierarchical multi-scale attention for semantic segmentation. arXiv preprint arXiv:2005.10821. · arXiv
- Van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9(11), 2579–2605.
- Vu, T. H., Jain, H., Bucher, M., Cord, M., & Pérez, P. (2019). Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPR.2019.00262 · DOI · Paper page
- Zhang, L., Wang, X., Yang, D., Sanford, T., Harmon, S., Turkbey, B., Roth, H., Myronenko, A., Xu, D., Xu Z., (2019). When unseen domain generalization is unnecessary? Rethinking data augmentation. arXiv preprint arXiv:1906.03347. · arXiv
- Zhou, K., Yang, Y., Qiao, Y., & Xiang, T. (2021). Mixstyle neural networks for domain generalization and adaptation. arXiv preprint arXiv:2107.02053. · arXiv
Metadata
- Authors
- Ma, S., Song, K., Niu, M., Tian, H., & Yan, Y.
- Affiliations
- Northeastern University (CN)
- Corresponding author
- Song, Kechen
- Yan, Yunhui
- Venue
- Journal of Intelligent Manufacturing
- Published
- 2022-11-27
- Volume
- 35
- Issue
- 1
- Pages
- 367-386
- ISSN
- 0956-5515 (ISSN-L)
- 1572-8145
- OpenAlex
- W4310276610
- Cited by
- 31
- References
- 37
- Citations by year
- 2026: 9 · 2025: 7 · 2024: 10 · 2023: 4 · 2022: 1
- FWCI
- 2.52
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Intelligent Manufacturing (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- domain generalization, cross-scale feature fusion, domain adversarial learning, domain shift, multi-scale feature extraction, distribution discrepancy
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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