MOSAIC: Mobile Segmentation via decoding Aggregated Information and encoded Context [article]

Weijun Wang, Andrew Howard
2021 arXiv   pre-print
We present a next-generation neural network architecture, MOSAIC, for efficient and accurate semantic image segmentation on mobile devices. MOSAIC is designed using commonly supported neural operations by diverse mobile hardware platforms for flexible deployment across various mobile platforms. With a simple asymmetric encoder-decoder structure which consists of an efficient multi-scale context encoder and a light-weight hybrid decoder to recover spatial details from aggregated information,
more » ... IC achieves new state-of-the-art performance while balancing accuracy and computational cost. Deployed on top of a tailored feature extraction backbone based on a searched classification network, MOSAIC achieves a 5% absolute accuracy gain surpassing the current industry standard MLPerf models and state-of-the-art architectures.
arXiv:2112.11623v1 fatcat:ayhux3gbybgf5edd7m77ov5l7u