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Author (up) Jiaolong Xu; Sebastian Ramos; Xu Hu; David Vazquez; Antonio Lopez edit   pdf
openurl 
  Title Multi-task Bilinear Classifiers for Visual Domain Adaptation Type Conference Article
  Year 2013 Publication Advances in Neural Information Processing Systems Workshop Abbreviated Journal  
  Volume Issue Pages  
  Keywords Domain Adaptation; Pedestrian Detection; ADAS  
  Abstract We propose a method that aims to lessen the significant accuracy degradation
that 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 classification
parameters 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.
 
  Address Lake Tahoe; Nevada; USA; December 2013  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference NIPSW  
  Notes ADAS; 600.054; 600.057; 601.217;ISE Approved no  
  Call Number ADAS @ adas @ XRH2013 Serial 2340  
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