|
Convoluted Mixture of Deep Experts for Robust Semantic Segmentation
Abhinav Valada, Ankit Dhall and Wolfram Burgard
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Workshop, State Estimation and Terrain Perception for All Terrain Mobile Robots
2016
valada16irosws.pdf
Notes:
Robust scene understanding of outdoor environments
using passive optical sensors is a critical problem characterized
by changing conditions throughout the day and across
seasons. The perception models on a robot should be able learn
features impervious to these factors in order to be operable
in the real-world. In this paper, we propose a convoluted
mixture of deep experts (CMoDE) model that enables a multistream
deep neural network architecture to learn features from
complementary modalities and spectra that are resilient to
commonly observed environmental disturbances. Our model
first adaptively weighs features from each of the individual
experts and then further learns fused representations that are
robust to these disturbances namely shadows, snow, rain, glare
and motion blur. We comprehensively evaluate the CMoDE
model against several other existing fusion approaches and show
that our proposed model exceeds the state-of-the-art.
BibTeX:
@inproceedings{valada16irosws,
author = {Abhinav Valada and Ankit Dhall and Wolfram Burgard},
title = {Convoluted Mixture of Deep Experts for Robust Semantic Segmentation},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Workshop, State Estimation and Terrain Perception for All Terrain Mobile Robots},
year = 2016,
month = oct,
url = {http://ais.informatik.uni-freiburg.de/publications/papers/valada16irosws.pdf},
address = {Daejeon, Korea}
}
|
|
Search for a specific document