Abstract

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 fifirst defifine a variety of situations and train a specialized object boundary detector for each of them using [10]. 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 classifification probabilities. In experiments on ImageNet [35], Microsoft COCO [24], and Pascal VOC 2012 segmentation [13] we show that our situational object boundary detection gives signifificant improvements over a monolithic approach. Additionally, our method substantially outperforms [17] on semantic contour detection on their SBD dataset.