A Deeper Look at Dataset Bias
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Barbara Caputo | Tinne Tuytelaars | Tatiana Tommasi | Novi Patricia | T. Tuytelaars | B. Caputo | T. Tommasi | N. Patricia
[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.