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Multi-task learning is commonly used in autonomous driving for solving various visual perception tasks. It offers significant benefits in terms of both performance and computational complexity. Current work on multi-task learning networks focus on processing a single input image and there is no known implementation of multi-task learning handling a sequence of images. In this work, we propose a multistream multi-task network to take advantage of using feature representations from precedingdoi:10.1109/cvprw.2019.00159 dblp:conf/cvpr/ChennupatiSYR19 fatcat:bpnqthy5wzh4xanjrukvbobiqi