ICNet for Real-Time Semantic Segmentation on High-Resolution Images

We focus on the challenging task of realtime 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 compressed-PSPNet-based 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 to quickly achieve high-quality segmentation. Our system yields realtime inference on a single GPU card with decent quality results evaluated on challenging Cityscapes dataset.