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The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

Learning to Adapt Structured Output Space for Semantic Segmentation

Tsai, Yi-Hsuan, Hung, Wei-Chih, Schulter, Samuel, Sohn, Kihyuk, Yang, Ming-Hsuan, Chandraker, Manmohan·2018·2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
DOI 10.1109/cvpr.2018.00780
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Crossref · 10.1109/cvpr.2018.00780

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  • A rigid alignment of two dispersive domains may further accumulate intra-domain variance.

    “In this context, a rigid alignment of two dispersive domains may further accumulate intra-domain variance (Tsai et al., 2018, Yan et al., 2019).”

    From Enabling country-scale land cover mapping with meter-resolution satellite imagery (2023) · cites this paper as Tsai et al., 2018

    The Introduction says feature alignment across domains can be difficult for semantic segmentation because high-dimensional features encode diverse visual cues. Section 6.1 (GTA5) also reports that feature-space adaptation can fail to match source and target distributions effectively. Neither passage says rigid alignment of dispersive domains accumulates intra-domain variance; no page numbers are available in our copy. (LLM, conf=0.79)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 31

  1. 10.1109/ICCV.2017.223 · DOI
  2. yu, 2016, Multi-scale context aggregation by dilated convolutions, ICLRE
  3. simonyan, 2015, Very deep convolutional networks for large-scale image recognition, ICLRE
  4. richter, 2016, Playing for data: Ground truth from computer games, ECCV
  5. radford, 2016, Unsupervised representation learning with deep convolutional generative adversarial networks, ICLRE
  6. 10.1109/ICCV.2015.463 · DOI
  7. 10.1109/CVPR.2017.299 · DOI
  8. 10.1109/ICCV.2017.630 · DOI
  9. goodfellow, 2014, Generative adversarial nets, NIPS
  10. 10.1109/CVPR.2017.660 · DOI · Paper page
  11. hoffman, 2017, CyCADA Cycle-Consistent Adversarial Domain Adaptation
  12. hoffman, 2016, Fcns in the wild Pixel-level adversarial and constraint-based adaptation
  13. hong, 2015, Decoupled deep neural network for semi-supervised semantic segmentation, NIPS
  14. 10.1109/ICCV.2017.287 · DOI
  15. 10.1109/CVPR.2017.181 · DOI
  16. kingma, 2015, Adam: A method for stochastic optimization, ICLRE
  17. papandreou, 2015, Weakly- and semi-supervised learning of a dcnn for semantic image segmentation, ICCV
  18. 10.1109/CVPR.2016.350 · DOI · Paper page
  19. maas, 2013, Rectifier nonlinearities improve neural network acoustic models, ICML
  20. 10.1109/ICCV.2017.220 · DOI
  21. 10.1109/ICCV.2015.209 · DOI
  22. 10.1109/ICCV.2015.191 · DOI
  23. ganin, 2016, Domain-adversarial training of neural networks, JMLR
  24. ganin, 2015, Unsupervised domain adaptation by backpropagation, ICML
  25. 10.1109/CVPR.2012.6248074 · DOI · Paper page
  26. lee, 2015, Deeply-supervised nets, AISTATS
  27. liu, 2016, Coupled generative adversarial networks, NIPS
  28. 10.1109/CVPR.2015.7298965 · DOI · Paper page
  29. 10.1109/ICCV.2015.162 · DOI · Paper page
  30. long, 2016, Unsupervised domain adaptation with residual transfer networks, NIPS
  31. long, 2015, Learning transferable features with deep adaptation networks, ICML

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Metadata

Authors
Tsai, Y.-H., Hung, W.-C., Schulter, S., Sohn, K., Yang, M.-H., & Chandraker, M.
Venue
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Publisher
IEEE
Published
2018-06
Pages
7472-7481
DOI
10.1109/cvpr.2018.00780
Cited by
1,430
References
11

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