@InProceedings{JiaolongXu2013,
author="Jiaolong Xu
and Sebastian Ramos
and Xu Hu
and David Vazquez
and Antonio Lopez",
title="Multi-task Bilinear Classifiers for Visual Domain Adaptation",
booktitle="Advances in Neural Information Processing Systems Workshop",
year="2013",
optkeywords="Domain Adaptation",
optkeywords="Pedestrian Detection",
optkeywords="ADAS",
abstract="We propose a method that aims to lessen the significant accuracy degradationthat a discriminative classifier can suffer when it is trained in a specific domain (source domain) and applied in a different one (target domain). The principal reason for this degradation is the discrepancies in the distribution of the features that feed the classifier in different domains. Therefore, we propose a domain adaptation method that maps the features from the different domains into a common subspace and learns a discriminative domain-invariant classifier within it. Our algorithm combines bilinear classifiers and multi-task learning for domain adaptation.The bilinear classifier encodes the feature transformation and classificationparameters by a matrix decomposition. In this way, specific feature transformations for multiple domains and a shared classifier are jointly learned in a multi-task learning framework. Focusing on domain adaptation for visual object detection, we apply this method to the state-of-the-art deformable part-based model for cross domain pedestrian detection. Experimental results show that our method significantly avoids the domain drift and improves the accuracy when compared to several baselines.",
optnote="ADAS; 600.054; 600.057; 601.217;ISE",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2340), last updated on Thu, 10 Nov 2016 12:24:02 +0100",
file=":http://refbase.cvc.uab.es/files/xrh2013.pdf:PDF"
}