  This paper presents a context-aware tracing strategy (CATS) for crisp edge
detection with deep edge detectors, based on an observation that the
localization ambiguity of deep edge detectors is mainly caused by the mixing
phenomenon of convolutional neural networks: feature mixing in edge
classification and side mixing during fusing side predictions. The CATS
consists of two modules: a novel tracing loss that performs feature unmixing by
tracing boundaries for better side edge learning, and a context-aware fusion
block that tackles the side mixing by aggregating the complementary merits of
learned side edges. Experiments demonstrate that the proposed CATS can be
integrated into modern deep edge detectors to improve localization accuracy.
With the vanilla VGG16 backbone, in terms of BSDS500 dataset, our CATS improves
the F-measure (ODS) of the RCF and BDCN deep edge detectors by 12% and 6%
respectively when evaluating without using the morphological non-maximal
suppression scheme for edge detection.
