Towards the Uncharted: Density-Descending Feature Perturbation for Semi-supervised Semantic Segmentation

TL;DR

This paper introduces Density-Descending Feature Perturbation (DDFP), a novel semi-supervised semantic segmentation method that leverages feature density estimation to guide perturbations towards low-density regions, improving classifier regularization.

Contribution

The paper proposes a new feature-level consistency framework using density estimation and perturbation to enhance semi-supervised segmentation performance.

Findings

Outperforms existing feature perturbation methods

Achieves state-of-the-art results on Pascal VOC and Cityscapes

Effectively regularizes decision boundaries using density-guided perturbations

Abstract

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.