# Contour-Aware Equipotential Learning for Semantic Segmentation

Source: [Contour-Aware Equipotential Learning for Semantic Segmentation](<https://www.sota2.com/research/paper/68fa6d8f1e209131cea3a9cf>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:37:22.464Z

Authors: Xu Yin, Dongbo Min, Yuchi Huo, Sung-Eui Yoon

Published: 2022-10-01T08:45:44.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Semantic Segmentation on Cityscapes (val)](<https://www.sota2.com/research/sota/semantic-segmentation-on-cityscapes-val.md>): Semantic segmentation; Cityscapes (val); mIoU: not recorded.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2210.00223>)

[PDF](<https://arxiv.org/pdf/2210.00223.pdf>)