  Intuitively, the appearance of true object boundaries varies from image to
image. Hence the usual monolithic approach of training a single boundary
predictor and applying it to all images regardless of their content is bound to
be suboptimal. In this paper we therefore propose situational object boundary
detection: We first define a variety of situations and train a specialized
object boundary detector for each of them using [Dollar and Zitnick 2013]. Then
given a test image, we classify it into these situations using its context,
which we model by global image appearance. We apply the corresponding
situational object boundary detectors, and fuse them based on the
classification probabilities. In experiments on ImageNet, Microsoft COCO, and
Pascal VOC 2012 segmentation we show that our situational object boundary
detection gives significant improvements over a monolithic approach.
Additionally, our method substantially outperforms [Hariharan et al. 2011] on
semantic contour detection on their SBD dataset.
