Abstract
Pseudo-labeling based methods are commonly employed for effectively utilizing unlabeled data in semi-supervised semantic image segmentation. It tends to select high-confidence pixels in images as pseudo labels and discard most of the pixels predicted with low confidence, but each pixel is valuable for accurate segmentation. Therefore, we propose a semi-supervised semantic image segmentation algorithm based on complementary reconfirmation mechanism (CR-Seg) to constrain the low-confidence pixels. Firstly, the predictions are divided into high-confidence and low-confidence pixels by a dynamic threshold. The High-confidence pixels supervise the predictions of the student model and guide the classifier to learn what they are. While the low-confidence pixels are equally important for model training, they are used to generate complementary reconfirmation loss for providing additional information as complementary labels. Our method achieves mIoU of 69.12, 73.84, 74.03, and 76.91% under 1/16, 1/8, 1/4, and 1/2 partitions of the classic PASCAL VOC 2012. The experimental results demonstrate that low-confidence pixels can provide more information to the model as complementary labels, thereby improving the model’s segmentation performance.
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Acknowledgment
This work is supported in part by the Key Program of NSFC (No. U1908214), 111 Project (No. D23006), the Scientific Research Fundation of Education Department of Liaoning Province (No. LJKMZ20221839), the Science and Technology Innovation Fund of Dalian (No. 2020JJ25CY001), Program for Innovative Research Team in University of Liaoning Province (LT2020015), Support Plan for Key Field Innovation Team of Dalian (2021RT06).
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Xiao, Y., Dong, J., Zhang, Q., Yi, P., Liu, R., Wei, X. (2024). Semi-supervised Semantic Segmentation with Complementary Reconfirmation Mechanism. In: Naik, N., Jenkins, P., Grace, P., Yang, L., Prajapat, S. (eds) Advances in Computational Intelligence Systems. UKCI 2023. Advances in Intelligent Systems and Computing, vol 1453. Springer, Cham. https://doi.org/10.1007/978-3-031-47508-5_15
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