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Cited by 420

The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.

Adaptive Pyramid Context Network for Semantic Segmentation

He, Junjun, Deng, Zhongying, Zhou, Lei, Wang, Yali, Qiao, Yu·2019·2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
DOI 10.1109/cvpr.2019.00770
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Abstract

Recent studies witnessed that context features can significantly improve the performance of deep semantic segmentation networks. Current context based segmentation methods differ with each other in how to construct context features and perform differently in practice. This paper firstly introduces three desirable properties of context features in segmentation task. Specially, we find that Global-guided Local Affinity (GLA) can play a vital role in constructing effective context features, while this property has been largely ignored in previous works. Based on this analysis, this paper proposes Adaptive Pyramid Context Network (APCNet) for semantic segmentation. APCNet adaptively constructs multi-scale contextual representations with multiple well-designed Adaptive Context Modules (ACMs). Specifically, each ACM leverages a global image representation as a guidance to estimate the local affinity coefficients for each sub-region, and then calculates a context vector with these affinities. We empirically evaluate our APCNet on three semantic segmentation and scene parsing datasets, including PASCAL VOC 2012, Pascal-Context, and ADE20K dataset. Experimental results show that APCNet achieves state-of-the-art performance on all three benchmarks, and obtains a new record 84.2% on PASCAL VOC 2012 test set without MS COCO pre-trained and any post-processing.

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Show all 18 references

Research works printed but not linked: 20

  1. 10.1109/CVPR.2018.00747 · DOI · Paper page
  2. 10.1109/CVPR.2017.75 · DOI · Paper page
  3. 10.1109/CVPR.2018.00199 · DOI · Paper page
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  5. jia, 2018, Psanet Point-wise spatial attention network for scene parsing
  6. 10.1109/CVPR.2018.00085 · DOI
  7. 10.1007/978-3-030-01219-9_37 · DOI · Paper page
  8. lin, 2014, Microsoft coco: Common objects in context, European Conference on Computer Vision, 740
  9. ronneberger, 2015, Unet: Convolutional networks for biomedical image segmentation, International Conference on Medical Image Computing and Computer-Assisted Intervention, 234
  10. 10.1109/TPAMI.2017.2699184 · DOI · Paper page
  11. 10.1109/CVPR.2017.39 · DOI · Paper page
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  13. 10.1109/ICCV.2015.191 · DOI · Paper page
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  15. arnab, 2016, Higher order conditional random fields in deep neural networks, European Conference on Computer Vision, 524
  16. 10.1109/CVPR.2015.7298965 · DOI · Paper page
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  19. paszke, 2017, On Automatic Differentiation
  20. 10.1109/ICCV.2015.178 · DOI · Paper page

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Metadata

Authors
He, J., Deng, Z., Zhou, L., Wang, Y., & Qiao, Y.
Venue
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Published
2019-06
Volume
2019
Pages
7511-7520
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2019.00770
OpenAlex
W2965391153
Cited by
420
References
18
FWCI
18.39
Type
conference paper
Language
en
Open access
closed
Licence

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CoChat. (2026). Citation check: Adaptive Pyramid Context Network for Semantic Segmentation. https://app.cochat.ai/papers/10.1109/cvpr.2019.00770
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He, J., Deng, Z., Zhou, L., Wang, Y., & Qiao, Y. (2019). Adaptive Pyramid Context Network for Semantic Segmentation. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.00770

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