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Most of the current boundary detection systems rely exclusively on low-levelfeatures, such as color and texture. However, perception studies suggest thathumans employ object-level reasoning when judging if a particular pixel is aboundary. Inspired by this observation, in this work we show how to predictboundaries by exploiting object-level features from a pretrainedobject-classification network. Our method can be viewed as a "High-for-Low"approach where high-level object features inform the low-level boundarydetection process. Our model achieves state-of-the-art performance on anestablished boundary detection benchmark and it is efficient to run.  Additionally, we show that due to the semantic nature of our boundaries wecan use them to aid a number of high-level vision tasks. We demonstrate thatusing our boundaries we improve the performance of state-of-the-art methods onthe problems of semantic boundary labeling, semantic segmentation and objectproposal generation. We can view this process as a "Low-for-High" scheme, wherelow-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 improveexisting state-of-the-art high-level vision methods on three distinct tasks.

High-for-Low and Low-for-High: Efficient Boundary Detection from Deep Object Features and its Applications to High-Level Vision