We present a ForkGAN for task-agnostic image translation that can boost multiple vision tasks in adverse weather conditions. Three tasks of image localization/retrieval, semantic image segmentation, and object detection are evaluated. The key challenge is achieving high-quality image translation without any explicit supervision, or task awareness. Our innovation is a fork-shape generator with one encoder and two decoders that disentangles the domain-specific and domain-invariant information. We force the cyclic translation between the weather conditions to go through a common encoding space, and make sure the encoding features reveal no information about the domains. Experimental results show our algorithm produces state-of-the-art image synthesis results and boost three vision tasks’ performances in adverse weathers.
CITATION STYLE
Zheng, Z., Wu, Y., Han, X., & Shi, J. (2020). ForkGAN: Seeing into the Rainy Night. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12348 LNCS, pp. 155–170). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-58580-8_10
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