We focus on the challenging task of real-time semantic segmentation in this paper. It finds many practical applications and yet is with fundamental difficulty of reducing a large portion of computation for pixel-wise label inference. We propose an image cascade network (ICNet) that incorporates multi-resolution branches under proper label guidance to address this challenge. We provide in-depth analysis of our framework and introduce the cascade feature fusion unit to quickly achieve high-quality segmentation. Our system yields real-time inference on a single GPU card with decent quality results evaluated on challenging datasets like Cityscapes, CamVid and COCO-Stuff.
CITATION STYLE
Zhao, H., Qi, X., Shen, X., Shi, J., & Jia, J. (2018). ICNet for Real-Time Semantic Segmentation on High-Resolution Images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11207 LNCS, pp. 418–434). Springer Verlag. https://doi.org/10.1007/978-3-030-01219-9_25
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