Semi-Supervised Semantic Segmentation via Derivative Label Propagation
This work addresses the annotation burden in semantic segmentation for computer vision applications, but it appears incremental as it builds on existing pseudo-labeling strategies.
The paper tackles the problem of unreliable pseudo-labels in semi-supervised semantic segmentation by proposing DerProp, a framework with derivative label propagation that regularizes similarity metrics, achieving state-of-the-art results in experiments.
Semi-supervised semantic segmentation, which leverages a limited set of labeled images, helps to relieve the heavy annotation burden. While pseudo-labeling strategies yield promising results, there is still room for enhancing the reliability of pseudo-labels. Hence, we develop a semi-supervised framework, namely DerProp, equipped with a novel derivative label propagation to rectify imperfect pseudo-labels. Our label propagation method imposes discrete derivative operations on pixel-wise feature vectors as additional regularization, thereby generating strictly regularized similarity metrics. Doing so effectively alleviates the ill-posed problem that identical similarities correspond to different features, through constraining the solution space. Extensive experiments are conducted to verify the rationality of our design, and demonstrate our superiority over other methods. Codes are available at https://github.com/ForawardStar/DerProp/.