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Cited by 183

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

A Relation-Augmented Fully Convolutional Network for Semantic Segmentation in Aerial Scenes

Mou, Lichao, Hua, Yuansheng, Zhu, Xiao Xiang·2019·2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
DOI 10.1109/cvpr.2019.01270
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  • Semantic segmentation models can predict dense classification maps for arbitrary-sized images in an end-to-end, pixels-to-pixels manner.

    “In contrast, semantic segmentation models can predict dense classification maps for arbitrary-sized images in an end-to-end, pixels-to-pixels manner (Wurm et al., 2019, Chen et al., 2020, Mou et al., 2019).”

    From Enabling country-scale land cover mapping with meter-resolution satellite imagery (2023) · cites this paper as Mou et al., 2019

    Section 3.1 (Overview; page unavailable) describes a fully convolutional network that upsamples feature maps to full resolution to produce segmentation maps. Section 2 (Related Work; page unavailable) also describes deep CNNs as enabling end-to-end aerial image parsing. Our copy does not establish that the model accepts arbitrary-sized images, so that part of the claim is unsupported. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Citations

Cites

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Research works printed but not linked: 33

  1. yao, 2012, Describing the scene as a whole: Joint object detection, scene classification and semantic segmentation, CVPR
  2. santoro, 2017, A simple neural network module for relational reasoning, Advances in Neural Information Processing Systems (NIPS)
  3. 10.1145/2833258.2833272 · DOI
  4. wainwright, 2008, Graphical models, exponential families, and variational inference, Foundations and Trends in Machine Learning, 1, 1, 10.1561/2200000001 · DOI
  5. 10.1109/TGRS.2016.2616585 · DOI
  6. 10.1109/TGRS.2014.2321423 · DOI
  7. simonyan, 2015, Very deep convolutional networks for large-scale image recognition, International Conference on Learning Representations (ICLR)
  8. 10.1109/CVPR.2017.660 · DOI · Paper page
  9. 10.1007/s11263-008-0140-x · DOI
  10. 10.1109/CVPR.2018.00378 · DOI · Paper page
  11. 10.1109/CVPR.2018.00745 · DOI · Paper page
  12. 10.1016/j.isprsjprs.2019.01.015 · DOI
  13. 10.1109/CVPR.2017.243 · DOI · Paper page
  14. 10.1109/TGRS.2017.2719738 · DOI
  15. ke, 2018, Adaptive affinity fields for semantic segmentation, European Conference on Computer Vision (ECCV)
  16. 10.1016/j.isprsjprs.2017.11.009 · DOI
  17. 10.1109/CVPR.2016.492 · DOI
  18. 10.1016/j.isprsjprs.2018.01.021 · DOI
  19. 10.1109/CVPR.2016.392 · DOI
  20. cheng, 2018, Depth estimation via affinity learned with convolutional spatial propagation network, European Conference on Computer Vision (ECCV)
  21. pan, 2018, Spatial as deep: Spatial CNN for traffic scene understanding, AAAI Conference on Artificial Intelligence (AAAI), 10.1609/aaai.v32i1.12301 · DOI
  22. 10.1109/CVPR.2016.314 · DOI
  23. 10.1023/A:1020249912095 · DOI
  24. liu, 2017, Learning affinity via spatial propagation networks, Advances in Neural Information Processing Systems (NIPS), 10.1007/978-3-319-70139-4 · DOI
  25. liu, 2015, Semantic image segmentation via deep parsing network, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 10.1007/978-1-4471-6741-9 · DOI
  26. liu, 2018, Switchable temporal propagation network, European Conference on Computer Vision (ECCV)
  27. zhou, 2018, Temporal relational reasoning in videos, European Conference on Computer Vision (ECCV)
  28. 10.1109/TGRS.2017.2740362 · DOI
  29. 10.1109/CVPR.2015.7298965 · DOI · Paper page
  30. 10.1109/MGRS.2017.2762307 · DOI · Paper page
  31. marcos, 2018, Learning deep structured active contours end-to-end, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)
  32. zhou, 2015, Object detectors emerge in deep scene CNNs, International Conference on Learning Representations (ICLR)
  33. 10.1109/CVPR.2016.26 · DOI

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Metadata

Authors
Mou, L., Hua, Y., & Zhu, X. X.
Affiliations
  • DLR&TUM
  • German Aerospace Center
  • Technical Univ. of Munich (TUM); German Aerospace Center (DLR)
Venue
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Published
2019-06
Pages
12408-12417
DOI
10.1109/cvpr.2019.01270
Cited by
183
References
8
Licence

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