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Joint Attention Mechanisms for Monocular Depth Estimation with Multi-Scale Convolutions and Adaptive Weight Adjustment
2020
IEEE Access
Monocular depth estimation is a fundamental problem for various vision applications, and is therefore gaining increasing attention in the field of computer vision. Though a great improvement has been made thanks to the rapid progress of deep convolutional neural networks, depth estimation of the object at finer details remains an unsatisfactory issue, especially in complex scenes that has rich structure information. In this paper, we proposed a deep end-to-end learning framework with the
doi:10.1109/access.2020.3030097
fatcat:yaei4lacpbaldfqyignca63rv4