Generating a robust representation of the environment is a crucial ability of learning agents. Deep learning based methods have greatly improved perception systems but still fail in challenging situations. These failures are often not solvable on the basis of a single image. In this work, we present a parameter-efficient temporal filtering concept which extends an existing single-frame segmentation model to work with multiple frames. The resulting recurrent architecture temporally filters representations on all abstraction levels in a hierarchical manner, while decoupling temporal dependencies from scene representation. Using a synthetic dataset, we show the ability of our model to cope with data perturbations and highlight the importance of recurrent and hierarchical filtering.
@article{arxiv.1810.02766,
title = {Hierarchical Recurrent Filtering for Fully Convolutional DenseNets},
author = {Jörg Wagner and Volker Fischer and Michael Herman and Sven Behnke},
journal= {arXiv preprint arXiv:1810.02766},
year = {2018}
}
Comments
In Proceedings of 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, Belgium, 2018