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Feilong Wu

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Proceedings Article | 12 April 2021 Presentation + Paper

Semi-supervised semantic segmentation via image-to-image translation

Feilong Wu, Suya You

Proceedings Volume 11729, 117290E (2021) https://doi.org/10.1117/12.2586052

KEYWORDS: Image segmentation, Statistical modeling, Performance modeling, Neural networks, Machine vision, Machine learning, Computer vision technology, Bridges

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Semantic segmentation is an important task in computer vision that aims to infer pixel-level semantic label information in images. Recently significant progresses have been made in deep neural networks-driven segmentation techniques, but these often require a large number of labels to supervise neural network training. Obtaining a sufficient number of labeled training data is challenging and sometimes impractical in real-world applications. This paper aims to study a semi-supervised semantic segmentation via image-to-image (I2I) translation technique. I2I is an emerging technique that maps an image from a domain to another domain. We uniquely treat the image semantic segmentation as an I2I translation task that infers semantic labels of objects (target domain) from input image (source domain) in a weakly supervised way. Particularly, we develop a two-pass strategy of I2I combining images with real and pseudo labels for semi-supervised model learning. The first pass uses unsupervised models to generate pseudo labels that combine with inputs to form pseudo-labeled samples. Since the pseudo-labeled images may undermine the quality of the model, they have to be specifically constrained in training by a noise correction framework to ensure good performance. Therefore, we boost the performance by incorporating both real and the pseudo labeled samples into the second pass to train a model based on a supervised architecture. Our goal is to bridge the gap between supervised and unsupervised learning for semantic object segmentation in practical. Extensive evaluations are conducted to demonstrate the efficiency and effectiveness of the proposed technique.

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