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Auxiliary Tasks in Multi-task Learning
Multi-task convolutional neural networks (CNNs) have shown impressive re...
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Deep Robust Single Image Depth Estimation Neural Network Using Scene Understanding
Single image depth estimation (SIDE) plays a crucial role in 3D computer...
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Deep 2.5D Vehicle Classification with Sparse SfM Depth Prior for Automated Toll Systems
Automated toll systems rely on proper classification of the passing vehi...
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Inferring Distributions Over Depth from a Single Image
When building a geometric scene understanding system for autonomous vehi...
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Depth Not Needed - An Evaluation of RGB-D Feature Encodings for Off-Road Scene Understanding by Convolutional Neural Network
Scene understanding for autonomous vehicles is a challenging computer vi...
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Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks
The training of many existing end-to-end steering angle prediction model...
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PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing
Depth estimation and scene parsing are two particularly important tasks ...
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MultiDepth: Single-Image Depth Estimation via Multi-Task Regression and Classification
We introduce MultiDepth, a novel training strategy and convolutional neural network (CNN) architecture that allows approaching single-image depth estimation (SIDE) as a multi-task problem. SIDE is an important part of road scene understanding. It, thus, plays a vital role in advanced driver assistance systems and autonomous vehicles. Best results for the SIDE task so far have been achieved using deep CNNs. However, optimization of regression problems, such as estimating depth, is still a challenging task. For the related tasks of image classification and semantic segmentation, numerous CNN-based methods with robust training behavior have been proposed. Hence, in order to overcome the notorious instability and slow convergence of depth value regression during training, MultiDepth makes use of depth interval classification as an auxiliary task. The auxiliary task can be disabled at test-time to predict continuous depth values using the main regression branch more efficiently. We applied MultiDepth to road scenes and present results on the KITTI depth prediction dataset. In experiments, we were able to show that end-to-end multi-task learning with both, regression and classification, is able to considerably improve training and yield more accurate results.
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