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We present a next-generation neural network architecture, MOSAIC, forefficient and accurate semantic image segmentation on mobile devices. MOSAIC isdesigned using commonly supported neural operations by diverse mobile hardwareplatforms for flexible deployment across various mobile platforms. With asimple asymmetric encoder-decoder structure which consists of an efficientmulti-scale context encoder and a light-weight hybrid decoder to recoverspatial details from aggregated information, MOSAIC achieves newstate-of-the-art performance while balancing accuracy and computational cost.Deployed on top of a tailored feature extraction backbone based on a searchedclassification network, MOSAIC achieves a 5% absolute accuracy gain surpassingthe current industry standard MLPerf models and state-of-the-art architectures.

MOSAIC: Mobile Segmentation via decoding Aggregated Information and encoded Context