A Deeper Look at Dataset Bias

[1]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[2]  Dong Xu,et al.  Exploiting Low-Rank Structure from Latent Domains for Domain Generalization , 2014, ECCV.

[3]  Trevor Darrell,et al.  LSDA: Large Scale Detection through Adaptation , 2014, NIPS.

[4]  Andrew Zisserman,et al.  Efficient On-the-fly Category Retrieval Using ConvNets and GPUs , 2014, ACCV.

[5]  Trevor Darrell,et al.  Part-Based R-CNNs for Fine-Grained Category Detection , 2014, ECCV.

[6]  Jitendra Malik,et al.  Analyzing the Performance of Multilayer Neural Networks for Object Recognition , 2014, ECCV.

[7]  Ivan Laptev,et al.  Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Andrew Zisserman,et al.  Return of the Devil in the Details: Delving Deep into Convolutional Nets , 2014, BMVC.

[9]  Stefan Carlsson,et al.  CNN Features Off-the-Shelf: An Astounding Baseline for Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[10]  Svetlana Lazebnik,et al.  Multi-scale Orderless Pooling of Deep Convolutional Activation Features , 2014, ECCV.

[11]  Tinne Tuytelaars,et al.  A Testbed for Cross-Dataset Analysis , 2014, ECCV Workshops.

[12]  Xiang Zhang,et al.  OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.

[13]  Trevor Darrell,et al.  One-Shot Adaptation of Supervised Deep Convolutional Models , 2013, ICLR.

[14]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[15]  Trevor Darrell,et al.  DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.

[16]  Kristen Grauman,et al.  Reshaping Visual Datasets for Domain Adaptation , 2013, NIPS.

[17]  Tinne Tuytelaars,et al.  Unsupervised Visual Domain Adaptation Using Subspace Alignment , 2013, 2013 IEEE International Conference on Computer Vision.

[18]  Marc Sebban,et al.  Iterative Self-Labeling Domain Adaptation for Linear Structured Image Classification , 2013, Int. J. Artif. Intell. Tools.

[19]  Kristen Grauman,et al.  Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation , 2013, ICML.

[20]  Sumit Chopra,et al.  DLID: Deep Learning for Domain Adaptation by Interpolating between Domains , 2013 .

[21]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[22]  Alexei A. Efros,et al.  Undoing the Damage of Dataset Bias , 2012, ECCV.

[23]  Trevor Darrell,et al.  Discovering Latent Domains for Multisource Domain Adaptation , 2012, ECCV.

[24]  Yuan Shi,et al.  Geodesic flow kernel for unsupervised domain adaptation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Boqing Gong,et al.  Overcoming Dataset Bias : An Unsupervised Domain Adaptation Approach , 2012 .

[26]  Alexei A. Efros,et al.  Unbiased look at dataset bias , 2011, CVPR 2011.

[27]  Dieter Fox,et al.  A large-scale hierarchical multi-view RGB-D object dataset , 2011, 2011 IEEE International Conference on Robotics and Automation.

[28]  Trevor Darrell,et al.  Adapting Visual Category Models to New Domains , 2010, ECCV.

[29]  Andrew W. Fitzgibbon,et al.  Efficient Object Category Recognition Using Classemes , 2010, ECCV.

[30]  Krista A. Ehinger,et al.  SUN database: Large-scale scene recognition from abbey to zoo , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[31]  Lorenzo Bruzzone,et al.  Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[33]  Koby Crammer,et al.  A theory of learning from different domains , 2010, Machine Learning.

[34]  Ali Farhadi,et al.  Describing objects by their attributes , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[35]  Christoph H. Lampert,et al.  Learning to detect unseen object classes by between-class attribute transfer , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[36]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[37]  Ivor W. Tsang,et al.  Domain adaptation from multiple sources via auxiliary classifiers , 2009, ICML '09.

[38]  G. Griffin,et al.  Caltech-256 Object Category Dataset , 2007 .

[39]  James J. Jiang A Literature Survey on Domain Adaptation of Statistical Classifiers , 2007 .

[40]  Koby Crammer,et al.  Analysis of Representations for Domain Adaptation , 2006, NIPS.

[41]  Bernhard Schölkopf,et al.  A Kernel Method for the Two-Sample-Problem , 2006, NIPS.

[42]  Pietro Perona,et al.  Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[43]  Bernt Schiele,et al.  Analyzing appearance and contour based methods for object categorization , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[44]  Bernt Schiele,et al.  Analyzing contour and appearance based methods for object categorization , 2003, CVPR 2003.

[45]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.