Abstract
Fully-convolutional neural networks (CNNs) for semantic segmentation dramatically improve performance using end-to-end learning on whole images in a supervised manner. The success of CNNs for semantic segmentation depends heavily on the pixel-level ground truth, which is labor-intensive in general. To partially solve this problem, domain adaptation techniques have been adapted to the two similar tasks for semantic segmentation, one of which is fully-labelled, while the other is unlabelled. Based on the adversarial learning method for domain adaptation in the context of semantic segmentation (AdaptSegNet), this paper proposes to employ the conditional random field (CRF) to refine the output of the segmentation network before domain adaptation. The proposed system fully integrates CRF model with CNNs, making it possible to train the whole system end-to-end with the usual backpropagation algorithm. Extensive experiments demonstrate the effectiveness of our framework under various domain adaptation settings, including synthetic-to-real scenarios.
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Acknowledgements
This work was partly supported the National Natural Science Foundation of China (Grant No. 61881240048, 61701252, 61876093, BK20181393), and HIRP Open 2018 Project of Huawei.
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Sun, Y., Wu, X., Zhou, Q., Zhang, S. (2020). Domain Adaptation for Semantic Segmentation with Conditional Random Field. In: Lu, H. (eds) Cognitive Internet of Things: Frameworks, Tools and Applications. ISAIR 2018. Studies in Computational Intelligence, vol 810. Springer, Cham. https://doi.org/10.1007/978-3-030-04946-1_46
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