1. Exploiting Minority Pseudo-Labels for Semi-Supervised Semantic Segmentation in Autonomous Driving
- Author
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Hong, Yuting, Xiao, Hui, Hao, Huazheng, Qiu, Xiaojie, Yao, Baochen, and Peng, Chengbin
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
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., Comment: 17 pages, 8 figures
- Published
- 2024