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Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services
Semantic segmentation constitutes the backbone of many mobile vision systems, spanning from robot navigation to augmented reality and teleconferencing. Frequently operating under stringent latency constraints within the limited resource envelope of embedded/mobile devices, optimising for efficient execution becomes important. To this end, we propose a framework for converting state-of-the-art segmentation models to MESS networks: specially trained CNNs that employ parametrised early exits alongdoi:10.1145/3498361.3538791 fatcat:udj2bntzj5ekbl7x2cdd5maudm