PT Unknown
AU Jiaolong Xu
   Sebastian Ramos
   Xu Hu
   David Vazquez
   Antonio Lopez
TI Multi-task Bilinear Classifiers for Visual Domain Adaptation
BT Advances in Neural Information Processing Systems Workshop
PY 2013
DE Domain Adaptation; Pedestrian Detection; ADAS
AB 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.
ER