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Found 4 papers, 1 papers with code
Background-Foreground Segmentation for Interior Sensing in Automotive Industry
In this work, we compare different statistical methods from the field of image segmentation to approach the problem of background-foreground segmentation in camera based interior sensing.
PrognoseNet: A Generative Probabilistic Framework for Multimodal Position Prediction given Context Information
Most of the current state-of-the-art Deep Learning approaches are trained on trajectory data to achieve this task.
RetinotopicNet: An Iterative Attention Mechanism Using Local Descriptors with Global Context
This redundancy is the main reason why CNNs need to be very deep in order to achieve the desired accuracy.
Training of Deep Neural Networks based on Distance Measures using RMSProp
However the problem was not really overcome in a fundamental way, since it is inherent to neural networks with activation functions based on dot products.
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