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Lecture Notes in Computer Science
In this paper, we propose a novel joint Task-Recursive Learning (TRL) framework for the closing-loop semantic segmentation and monocular depth estimation tasks. TRL can recursively refine the results of both tasks through serialized task-level interactions. In order to mutually-boost for each other, we encapsulate the interaction into a specific Task-Attentional Module (TAM) to adaptively enhance some counterpart patterns of both tasks. Further, to make the inference more credible, we propagatedoi:10.1007/978-3-030-01249-6_15 fatcat:aikjy2xbsjabjfczjo2vwuspdi