iccv iccv2013 iccv2013-52 iccv2013-52-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Adriana Kovashka, Kristen Grauman
Abstract: Current methods learn monolithic attribute predictors, with the assumption that a single model is sufficient to reflect human understanding of a visual attribute. However, in reality, humans vary in how they perceive the association between a named property and image content. For example, two people may have slightly different internal models for what makes a shoe look “formal”, or they may disagree on which of two scenes looks “more cluttered”. Rather than discount these differences as noise, we propose to learn user-specific attribute models. We adapt a generic model trained with annotations from multiple users, tailoring it to satisfy user-specific labels. Furthermore, we propose novel techniques to infer user-specific labels based on transitivity and contradictions in the user’s search history. We demonstrate that adapted attributes improve accuracy over both existing monolithic models as well as models that learn from scratch with user-specific data alone. In addition, we show how adapted attributes are useful to personalize image search, whether with binary or relative attributes.
[1] Y. Aytar and A. Zisserman. Tabula Rasa: Model Transfer for Object Category Detection. In ICCV, 2011.
[2] T. Berg, A. Berg, and J. Shih. Automatic Attribute Discovery and Characterization from Noisy Web Data. In ECCV, 2010.
[3] S. Branson, C. Wah, F. Schroff, B. Babenko, P. Welinder, P. Perona, and S. Belongie. Visual Recognition with Humans in the Loop. In
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19] ECCV, 2010. W. Curran, T. Moore, T. Kulesza, W.-K. Wong, S. Todorovic, S. Stumpf, R. White, and M. Burnett. Towards Recognizing ”Cool”: Can End Users Help Computer Vision Recognize Subjective Attributes or Objects in Images? In IUI, 2012. I. Endres, A. Farhadi, D. Hoiem, and D. Forsyth. The Benefits and Challenges of Collecting Richer Object Annotations. In ACVHL, 2010. A. Farhadi, I. Endres, D. Hoiem, and D. Forsyth. Describing Objects by Their Attributes. In CVPR, 2009. J. Gauvain and C.-H. Lee. Maximum A Posterior Estimation For Multivariate Gaussian Mixture Observations Of Markov Chains. IEEE Transactions on Speech and Audio Processing, 2, 1994. B. Geng, L. Yang, C. Xu, and X.-S. Hua. Ranking Model Adaptation for Domain-Specific Search. IEEE TKDE, March 2010. B. Gong, Y. Shi, F. Sha, and K. Grauman. Geodesic Flow Kernel for Unsupervised Domain Adaptation. In CVPR, 2012. R. Gopalan, R. Li, and R. Chellapa. Domain Adaptation for Object Recognition: An Unsupervised Approach. In ICCV, 2011. T. Joachims. Optimizing Search Engines Using Clickthrough Data. In KDD, 2002. A. Kovashka and K. Grauman. Attribute Pivots for Guiding Relevance Feedback in Image Search. In ICCV, 2013. A. Kovashka, D. Parikh, and K. Grauman. WhittleSearch: Image Search with Relative Attribute Feedback. In CVPR, 2012. N. Kumar, A. C. Berg, P. N. Belhumeur, and S. K. Nayar. Describable Visual Attributes for Face Verification and Image Search. PAMI, 2011. C. Lampert, H. Nickisch, and S. Harmeling. Learning to Detect Unseen Object Classes by Between-class Attribute Transfer. In CVPR, 2009. D. Parikh and K. Grauman. Relative Attributes. In ICCV, 2011. A. Parkash and D. Parikh. Attributes for Classifier Feedback. In ECCV, 2012. G. Pasi. Issues in Personalizing Information Retrieval. IEEE Intelligent Informatics Bulletin, 2010. G. Patterson and J. Hays. SUN Attribute Database: Discovering, Annotating, and Recognizing Scene Attributes. In CVPR, 2012.
[20] K. Saenko, B. Kulis, M. Fritz, and T. Darrell. Adapting Visual Category Models to New Domains. In ECCV, 2010.
[21] W. Scheirer, N. Kumar, P. Belhumeur, and T. Boult. Multi-Attribute Spaces: Calibration for Attribute Fusion and Similarity Search. In CVPR, 2012.
[22] A. Shrivastava, S. Singh, and A. Gupta. Constrained SemiSupervised Learning using Attributes and Comparative Attributes. In ECCV, 2012.
[23] B. Siddiquie, R. Feris, and L. Davis. Image Ranking and Retrieval Based on Multi-Attribute Queries. In CVPR, 2011.
[24] M. Stark, M. Goesele, and B. Schiele. A Shape-based Object Class Model for Knowledge Transfer. In ICCV, 2009.
[25] Y. Tian and J. Zhu. Learning from Crowds in the Presence of Schools of Thought. In KDD, 2012.
[26] S. Tong and E. Chang. Support Vector Machine Active Learning for Image Retrieval. In ACM Multimedia, 2001.
[27] P. Welinder, S. Branson, S. Belongie, and P. Perona. The Multidimensional Wisdom of Crowds. In NIPS, 2010.
[28] J. Yang, R. Yan, and A. G. Hauptmann. Adapting SVM Classifiers to Data with Shifted Distributions. In ICDM Workshops, 2007. 33443392