  Relying on large-scale training data with pixel-level labels, previous edge
detection methods have achieved high performance. However, it is hard to
manually label edges accurately, especially for large datasets, and thus the
datasets inevitably contain noisy labels. This label-noise issue has been
studied extensively for classification, while still remaining under-explored
for edge detection. To address the label-noise issue for edge detection, this
paper proposes to learn Pixel-level NoiseTransitions to model the
label-corruption process. To achieve it, we develop a novel Pixel-wise Shift
Learning (PSL) module to estimate the transition from clean to noisy labels as
a displacement field. Exploiting the estimated noise transitions, our model,
named PNT-Edge, is able to fit the prediction to clean labels. In addition, a
local edge density regularization term is devised to exploit local structure
information for better transition learning. This term encourages learning large
shifts for the edges with complex local structures. Experiments on SBD and
Cityscapes demonstrate the effectiveness of our method in relieving the impact
of label noise. Codes are available at https://github.com/DREAMXFAR/PNT-Edge.
