  With increasing demands for high-quality semantic segmentation in the
industry, hard-distinguishing semantic boundaries have posed a significant
threat to existing solutions. Inspired by real-life experience, i.e., combining
varied observations contributes to higher visual recognition confidence, we
present the equipotential learning (EPL) method. This novel module transfers
the predicted/ground-truth semantic labels to a self-defined potential domain
to learn and infer decision boundaries along customized directions. The
conversion to the potential domain is implemented via a lightweight
differentiable anisotropic convolution without incurring any parameter
overhead. Besides, the designed two loss functions, the point loss and the
equipotential line loss implement anisotropic field regression and
category-level contour learning, respectively, enhancing prediction
consistencies in the inter/intra-class boundary areas. More importantly, EPL is
agnostic to network architectures, and thus it can be plugged into most
existing segmentation models. This paper is the first attempt to address the
boundary segmentation problem with field regression and contour learning.
Meaningful performance improvements on Pascal Voc 2012 and Cityscapes
demonstrate that the proposed EPL module can benefit the off-the-shelf fully
convolutional network models when recognizing semantic boundary areas. Besides,
intensive comparisons and analysis show the favorable merits of EPL for
distinguishing semantically-similar and irregular-shaped categories.
