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 existing 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.

Keywords

Pascal (unit)Computer scienceSegmentationObject detectionArtificial intelligenceExploitContext modelContext (archaeology)PixelImage segmentationObject (grammar)Pattern recognition (psychology)Machine learningComputer visionGeography

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Year
2014
Type
article
Citations
1424
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Roozbeh Mottaghi, Xianjie Chen, Xiaobai Liu et al. (2014). The Role of Context for Object Detection and Semantic Segmentation in the Wild. . https://doi.org/10.1109/cvpr.2014.119

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DOI
10.1109/cvpr.2014.119