  Semi-supervised semantic segmentation allows model to mine effective
supervision from unlabeled data to complement label-guided training. Recent
research has primarily focused on consistency regularization techniques,
exploring perturbation-invariant training at both the image and feature levels.
In this work, we proposed a novel feature-level consistency learning framework
named Density-Descending Feature Perturbation (DDFP). Inspired by the
low-density separation assumption in semi-supervised learning, our key insight
is that feature density can shed a light on the most promising direction for
the segmentation classifier to explore, which is the regions with lower
density. We propose to shift features with confident predictions towards
lower-density regions by perturbation injection. The perturbed features are
then supervised by the predictions on the original features, thereby compelling
the classifier to explore less dense regions to effectively regularize the
decision boundary. Central to our method is the estimation of feature density.
To this end, we introduce a lightweight density estimator based on normalizing
flow, allowing for efficient capture of the feature density distribution in an
online manner. By extracting gradients from the density estimator, we can
determine the direction towards less dense regions for each feature. The
proposed DDFP outperforms other designs on feature-level perturbations and
shows state of the art performances on both Pascal VOC and Cityscapes dataset
under various partition protocols. The project is available at
https://github.com/Gavinwxy/DDFP.
