  We investigate the reasons why context in object detection has limited
utility by isolating and evaluating the predictive power of different context
cues under ideal conditions in which context provided by an oracle. Based on
this study, we propose a region-based context re-scoring method with dynamic
context selection to remove noise and emphasize informative context. We
introduce latent indicator variables to select (or ignore) potential contextual
regions, and learn the selection strategy with latent-SVM. We conduct
experiments to evaluate the performance of the proposed context selection
method on the SUN RGB-D dataset. The method achieves a significant improvement
in terms of mean average precision (mAP), compared with both appearance based
detectors and a conventional context model without the selection scheme.
