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.
|Title of host publication||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Publisher||IEEE Computer Society|
|Number of pages||8|
|ISBN (Electronic)||9781479951178, 9781479951178|
|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
|Name||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Other||27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014|
|Period||14/6/23 → 14/6/28|
Bibliographical notePublisher Copyright:
© 2014 IEEE.
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition