The role of context for object detection and semantic segmentation in the wild

Roozbeh Mottaghi; Xianjie Chen; Xiaobai Liu; Nam Gyu Cho; Seong Whan Lee; Sanja Fidler; Raquel Urtasun; Alan Yuille

doi:10.1109/CVPR.2014.119

The role of context for object detection and semantic segmentation in the wild

Roozbeh Mottaghi^*
, Xianjie Chen
, Xiaobai Liu
, Nam Gyu Cho
, Seong Whan Lee
, Sanja Fidler
, Raquel Urtasun
, Alan Yuille

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1288 Citations (Scopus)

Abstract

In this paper we study the role of context in existing state-of-the-art detection and segmentation approaches. Towards this goal, we label every pixel of PASCAL VOC 2010 detection challenge with a semantic category. We believe this data will provide plenty of challenges to the community, as it contains 520 additional classes for semantic segmentation and object detection. Our analysis shows that nearest neighbor based approaches perform poorly on semantic segmentation of contextual classes, showing the variability of PASCAL imagery. Furthermore, improvements of exist ing contextual models for detection is rather modest. In order to push forward the performance in this difficult scenario, we propose a novel deformable part-based model, which exploits both local context around each candidate detection as well as global context at the level of the scene. We show that this contextual reasoning significantly helps in detecting objects at all scales.

Original language	English
Title of host publication	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Publisher	IEEE Computer Society
Pages	891-898
Number of pages	8
ISBN (Electronic)	9781479951178, 9781479951178
DOIs	https://doi.org/10.1109/CVPR.2014.119
Publication status	Published - 2014 Sept 24
Event	27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 - Columbus, United States Duration: 2014 Jun 23 → 2014 Jun 28

Publication series

Name	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)	1063-6919

Other

Other	27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014
Country/Territory	United States
City	Columbus
Period	14/6/23 → 14/6/28

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition

Access to Document

10.1109/CVPR.2014.119

Cite this

Mottaghi, R., Chen, X., Liu, X., Cho, N. G., Lee, S. W., Fidler, S., Urtasun, R., & Yuille, A. (2014). The role of context for object detection and semantic segmentation in the wild. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 891-898). Article 6909514 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). IEEE Computer Society. https://doi.org/10.1109/CVPR.2014.119

The role of context for object detection and semantic segmentation in the wild. / Mottaghi, Roozbeh; Chen, Xianjie; Liu, Xiaobai et al.
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2014. p. 891-898 6909514 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Mottaghi, R, Chen, X, Liu, X, Cho, NG, Lee, SW, Fidler, S, Urtasun, R & Yuille, A 2014, The role of context for object detection and semantic segmentation in the wild. in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition., 6909514, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, pp. 891-898, 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, United States, 14/6/23. https://doi.org/10.1109/CVPR.2014.119

Mottaghi R, Chen X, Liu X, Cho NG, Lee SW, Fidler S et al. The role of context for object detection and semantic segmentation in the wild. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society. 2014. p. 891-898. 6909514. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). doi: 10.1109/CVPR.2014.119

Mottaghi, Roozbeh ; Chen, Xianjie ; Liu, Xiaobai et al. / The role of context for object detection and semantic segmentation in the wild. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2014. pp. 891-898 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).

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abstract = "In this paper we study the role of context in existing state-of-the-art detection and segmentation approaches. Towards this goal, we label every pixel of PASCAL VOC 2010 detection challenge with a semantic category. We believe this data will provide plenty of challenges to the community, as it contains 520 additional classes for semantic segmentation and object detection. Our analysis shows that nearest neighbor based approaches perform poorly on semantic segmentation of contextual classes, showing the variability of PASCAL imagery. Furthermore, improvements of exist ing contextual models for detection is rather modest. In order to push forward the performance in this difficult scenario, we propose a novel deformable part-based model, which exploits both local context around each candidate detection as well as global context at the level of the scene. We show that this contextual reasoning significantly helps in detecting objects at all scales.",

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The role of context for object detection and semantic segmentation in the wild

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