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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

344 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 languageEnglish
Title of host publicationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PublisherIEEE Computer Society
Pages891-898
Number of pages8
ISBN (Print)9781479951178, 9781479951178
DOIs
Publication statusPublished - 2014 Jan 1
Event27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 - Columbus, United States
Duration: 2014 Jun 232014 Jun 28

Other

Other27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014
CountryUnited States
CityColumbus
Period14/6/2314/6/28

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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  • 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). [6909514] IEEE Computer Society. https://doi.org/10.1109/CVPR.2014.119