Most of the current boundary detection systems rely exclusively on low-level
features, such as color and texture. However, perception studies suggest that
humans employ object-level reasoning when judging if a particular pixel is a
boundary. Inspired by this observation, in this work we show how to predict
boundaries by exploiting object-level features from a pretrained
object-classification network. Our method can be viewed as a "High-for-Low"
approach where high-level object features inform the low-level boundary
detection process. Our model achieves state-of-the-art performance on an
established boundary detection benchmark and it is efficient to run.
Additionally, we show that due to the semantic nature of our boundaries we
can use them to aid a number of high-level vision tasks. We demonstrate that
using our boundaries we improve the performance of state-of-the-art methods on
the problems of semantic boundary labeling, semantic segmentation and object
proposal generation. We can view this process as a "Low-for-High" scheme, where
low-level boundaries aid high-level vision tasks.
Thus, our contributions include a boundary detection system that is accurate,
efficient, generalizes well to multiple datasets, and is also shown to improve
existing state-of-the-art high-level vision methods on three distinct tasks.

该篇论文提出了一种利用对象级特征进行边界检测的方法，采用了 “高层次向低层次” 和 “低层次向高层次” 的方案，得到了较好的性能，并且可以用于辅助高层次视觉任务。