Exploiting Minority Pseudo-Labels for Semi-Supervised Semantic Segmentation in Autonomous Driving
<|reference_start|>Exploiting Minority Pseudo-Labels for Semi-Supervised Semantic Segmentation in Autonomous Driving: With the advancement of autonomous driving, semantic segmentation has achieved remarkable progress. The training of such networks heavily relies on image annotations, which are very expensive to obtain. Semi-supervised learning can utilize both labeled data and unlabeled data with the help of pseudo-labels. However, in many real-world scenarios where classes are imbalanced, majority classes often play a dominant role during training and the learning quality of minority classes can be undermined. To overcome this limitation, we propose a synergistic training framework, including a professional training module to enhance minority class learning and a general training module to learn more comprehensive semantic information. Based on a pixel selection strategy, they can iteratively learn from each other to reduce error accumulation and coupling. In addition, a dual contrastive learning with anchors is proposed to guarantee more distinct decision boundaries. In experiments, our framework demonstrates superior performance compared to state-of-the-art methods on benchmark datasets.<|reference_end|>
@article{hong2024exploiting,
    title={Exploiting Minority Pseudo-Labels for Semi-Supervised Semantic
  Segmentation in Autonomous Driving},
    author={Yuting Hong, Hui Xiao, Huazheng Hao, Xiaojie Qiu, Baochen Yao and
  Chengbin Peng},
    journal={arXiv preprint arXiv:2409.12680},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12680},
    primaryClass={cs.CV}
}