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Using Unreliable Pseudo-Labels for Label-Efficient Semantic Segmentation

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Abstract

The crux of label-efficient semantic segmentation is to produce high-quality pseudo-labels to leverage a large amount of unlabeled or weakly labeled data. A common practice is to select the highly confident predictions as the pseudo-ground-truths for each pixel, but it leads to a problem that most pixels may be left unused due to their unreliability. However, we argue that every pixel matters to the model training, even those unreliable and ambiguous pixels. Intuitively, an unreliable prediction may get confused among the top classes, however, it should be confident about the pixel not belonging to the remaining classes. Hence, such a pixel can be convincingly treated as a negative key to those most unlikely categories. Therefore, we develop an effective pipeline to make sufficient use of unlabeled data. Concretely, we separate reliable and unreliable pixels via the entropy of predictions, push each unreliable pixel to a category-wise queue that consists of negative keys, and manage to train the model with all candidate pixels. Considering the training evolution, we adaptively adjust the threshold for the reliable-unreliable partition. Experimental results on various benchmarks and training settings demonstrate the superiority of our approach over the state-of-the-art alternatives.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the PASCAL VOC 2012(http://host.robots.ox.ac.uk/pascal/VOC/), the SBD(https://ieeexplore.ieee.org/abstract/document/6126343), the GTA5(https://arxiv.org/pdf/1608.02192v1.pdf), the Synthia(https://synthia-dataset.net/), and the Cityscapes(https://www.cityscapes-dataset.com/).

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Acknowledgements

This work was supported in part by the National Key R&D Program of China (No. 2022ZD0116500), the National Natural Science Foundation of China (No. U21B2042, No. 62320106010), the 2035 Innovation Program of CAS, and the InnoHK program.

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Authors and Affiliations

  1. New Laboratory of Pattern Recognition, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Haochen Wang & Zhaoxiang Zhang

  2. University of Chinese Academy of Sciences, Beijing, China

    Haochen Wang & Zhaoxiang Zhang

  3. Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Hong Kong, China

    Junsong Fan, Yuxi Wang & Zhaoxiang Zhang

  4. Shanghai Jiao Tong University, Shanghai, China

    Yuchao Wang

  5. Chinese University of Hong Kong, Hong Kong, China

    Yujun Shen

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Communicated by Jifeng Dai.

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Wang, H., Wang, Y., Shen, Y. et al. Using Unreliable Pseudo-Labels for Label-Efficient Semantic Segmentation. Int J Comput Vis 133, 1476–1498 (2025). https://doi.org/10.1007/s11263-024-02229-x

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