Semi-Supervised Semantic Segmentation via Derivative Label Propagation

• URL: http://arxiv.org/abs/2508.02254v1
• Date: Mon, 04 Aug 2025 10:01:12 GMT
• Title: Semi-Supervised Semantic Segmentation via Derivative Label Propagation
• Authors: Yuanbin Fu, Xiaojie Guo,
• Abstract summary: We develop a semi-supervised framework, namely DerProp, equipped with a novel derivative label propagation to rectify imperfect pseudo-labels.<n>Our label propagation method imposes discrete derivative operations on pixel-wise feature vectors as additional regularization, thereby generating strictly regularized similarity metrics.
• Score: 9.46066115792446
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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/.

Related papers

• Reduction-based Pseudo-label Generation for Instance-dependent Partial Label Learning [41.345794038968776]
  We propose to leverage reduction-based pseudo-labels to alleviate the influence of incorrect candidate labels. We show that reduction-based pseudo-labels exhibit greater consistency with the Bayes optimal classifier compared to pseudo-labels directly generated from the predictive model.
  arXiv  Detail & Related papers  (2024-10-28T07:32:20Z)
• Labeled-to-Unlabeled Distribution Alignment for Partially-Supervised Multi-Organ Medical Image Segmentation [30.953837550398884]
  Partially-supervised multi-organ medical image segmentation aims to develop a unified semantic segmentation model. We propose a labeled-to-unlabeled distribution alignment framework that aligns feature distributions and enhances discriminative capability. Our method outperforms the state-of-the-art partially-supervised methods by a considerable margin.
  arXiv  Detail & Related papers  (2024-09-05T03:55:37Z)
• Inaccurate Label Distribution Learning with Dependency Noise [52.08553913094809]
  We introduce the Dependent Noise-based Inaccurate Label Distribution Learning (DN-ILDL) framework to tackle the challenges posed by noise in label distribution learning. We show that DN-ILDL effectively addresses the ILDL problem and outperforms existing LDL methods.
  arXiv  Detail & Related papers  (2024-05-26T07:58:07Z)
• HPL-ESS: Hybrid Pseudo-Labeling for Unsupervised Event-based Semantic Segmentation [47.271784693700845]
  We propose a novel hybrid pseudo-labeling framework for unsupervised event-based semantic segmentation, HPL-ESS, to alleviate the influence of noisy pseudo labels. Our proposed method outperforms existing state-of-the-art methods by a large margin on the DSEC-Semantic dataset.
  arXiv  Detail & Related papers  (2024-03-25T14:02:33Z)
• Scribble Hides Class: Promoting Scribble-Based Weakly-Supervised Semantic Segmentation with Its Class Label [16.745019028033518]
  We propose a class-driven scribble promotion network, which utilizes both scribble annotations and pseudo-labels informed by image-level classes and global semantics for supervision. Experiments on the ScribbleSup dataset with different qualities of scribble annotations outperform all the previous methods, demonstrating the superiority and robustness of our method.
  arXiv  Detail & Related papers  (2024-02-27T14:51:56Z)
• All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentation [67.30502812804271]
  Pseudo-labels are widely employed in weakly supervised 3D segmentation tasks where only sparse ground-truth labels are available for learning. We propose a novel learning strategy to regularize the generated pseudo-labels and effectively narrow the gaps between pseudo-labels and model predictions.
  arXiv  Detail & Related papers  (2023-05-25T08:19:31Z)
• Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection [149.23913018423022]
  Weakly supervised video anomaly detection aims to identify abnormal events in videos using only video-level labels. Two-stage self-training methods have achieved significant improvements by self-generating pseudo labels. We propose an enhancement framework by exploiting completeness and uncertainty properties for effective self-training.
  arXiv  Detail & Related papers  (2022-12-08T05:53:53Z)
• Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspective [89.5370481649529]
  We propose a label distribution perspective for PU learning in this paper. Motivated by this, we propose to pursue the label distribution consistency between predicted and ground-truth label distributions. Experiments on three benchmark datasets validate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-12-06T07:38:29Z)
• Label Distribution Learning via Implicit Distribution Representation [12.402054374952485]
  In this paper, we introduce the implicit distribution in the label distribution learning framework to characterize the uncertainty of each label value. Specifically, we use deep implicit representation learning to construct a label distribution matrix with Gaussian prior constraints. Each row component of the label distribution matrix is transformed into a standard label distribution form by using the self-attention algorithm.
  arXiv  Detail & Related papers  (2022-09-28T04:13:53Z)
• Distribution-Aware Semantics-Oriented Pseudo-label for Imbalanced Semi-Supervised Learning [80.05441565830726]
  This paper addresses imbalanced semi-supervised learning, where heavily biased pseudo-labels can harm the model performance. We propose a general pseudo-labeling framework to address the bias motivated by this observation. We term the novel pseudo-labeling framework for imbalanced SSL as Distribution-Aware Semantics-Oriented (DASO) Pseudo-label.
  arXiv  Detail & Related papers  (2021-06-10T11:58:25Z)
• MatchGAN: A Self-Supervised Semi-Supervised Conditional Generative Adversarial Network [51.84251358009803]
  We present a novel self-supervised learning approach for conditional generative adversarial networks (GANs) under a semi-supervised setting. We perform augmentation by randomly sampling sensible labels from the label space of the few labelled examples available. Our method surpasses the baseline with only 20% of the labelled examples used to train the baseline.
  arXiv  Detail & Related papers  (2020-06-11T17:14:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.