The Role of Context for Object Detection and Semantic Segmentation in the Wild

@article{Mottaghi2014TheRO,
  title={The Role of Context for Object Detection and Semantic Segmentation in the Wild},
  author={R. Mottaghi and X. Chen and Xiaobai Liu and Nam-Gyu Cho and S. Lee and S. Fidler and R. Urtasun and A. Yuille},
  journal={2014 IEEE Conference on Computer Vision and Pattern Recognition},
  year={2014},
  pages={891-898}
}
  • R. Mottaghi, X. Chen, +5 authors A. Yuille
  • Published 2014
  • Computer Science
  • 2014 IEEE Conference on Computer Vision and Pattern Recognition
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… Expand
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References

SHOWING 1-10 OF 47 REFERENCES
An empirical study of context in object detection
  • 442
  • PDF
Bottom-Up Segmentation for Top-Down Detection
  • 131
  • PDF
Describing the scene as a whole: Joint object detection, scene classification and semantic segmentation
  • 373
  • PDF
Analyzing Semantic Segmentation Using Hybrid Human-Machine CRFs
  • 34
  • PDF
Exploiting hierarchical context on a large database of object categories
  • 306
  • Highly Influential
  • PDF
Extracting adaptive contextual cues from unlabeled regions
  • 46
  • PDF
Learning Spatial Context: Using Stuff to Find Things
  • 439
  • PDF
A Statistical Model for General Contextual Object Recognition
  • 317
  • PDF
Object Recognition by Sequential Figure-Ground Ranking
  • 120
  • PDF
...
1
2
3
4
5
...