VisKE: Visual knowledge extraction and question answering by visual verification of relation phrases

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 07-12-June-2015, Issue , 2015, Pages 1456-1464

  Sadeghi, Fereshteh ^a   Divvala, Santosh K ^a,b   Farhadi, Ali ^a,b  

^a UNIVERSITY OF WASHINGTON   (United States)

^b Allen Institute for Artificial Intelligence   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; EXTRACTION;

HUMAN SUPERVISION; KNOWLEDGE BASIS; KNOWLEDGE EXTRACTION; LARGE-SCALE ANALYSIS; QUESTION ANSWERING; SPATIAL CONSISTENCY; VISUAL KNOWLEDGE; VISUAL VERIFICATION;

PATTERN RECOGNITION;

EID: 84959184467     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7298752     Document Type: Conference Paper

References (41)

1. A. Akbik and T. Michael. The weltmodell: A data-driven commonsense knowledge base. In LREC, 2014
2. M. Banko et al. Open information extraction from the web. In IJCAI, 2007
3. J. Berant, A. Chou, R. Frostig, and P. Liang. Semantic pars-ing on freebase from question-answer pairs. In EMNLP, 2013
4. A. Carlson, J. Betteridge, E. R. Hruschka, Jr., and T. M. Mitchell. Coupling semi-supervised learning of categories and relations. In NAACL, 2009
5. A. Carlson et al. Toward an architecture for never-ending language learning. In AAAI, 2010
6. X. Chen, A. Shrivastava, and A. Gupta. NEIL: Extracting visual knowledge from web data. In ICCV, 2013
7. P. Clark, P. Harrison, and N. Balasubramanian. A study of the knowledge base requirements for passing an elementary science test. In AKBC, 2013
8. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, 2009
9. S. Divvala, A. Farhadi, and C. Guestrin. Learning everything about anything: Webly-supervised visual concept learning. In CVPR, 2014
10. C. Doersch, A. Gupta, and A. A. Efros. Mid-level visual element discovery as discriminative mode seeking. In NIPS. 2013
11. D. Downey, O. Etzioni, and S. Soderland. A probabilistic model of redundancy in information extraction. In IJCAI, 2005
12. M. Everingham et al. The PASCAL Visual Object Classes (VOC) challenge. In IJCV, 2010
13. A. Fader, S. Soderland, and O. Etzioni. Identifying relations for open information extraction. In EMNLP, 2011
14. A. Fader, L. Zettlemoyer, and O. Etzioni. Open question answering over curated and extracted knowledge bases. In KDD, 2014
15. A. Farhadi, I. Endres, D. Hoiem, and D. Forsyth. Describing objects by their attributes. In CVPR, 2009
16. P. Felzenszwalb et al. Object detection with discriminatively trained part based models. PAMI, 2010
17. R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich fea-ture hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014
18. P. Grice. Logic and conversation. In Speech Acts, 1975
19. A. Gupta, A. Kembhavi, and L. Davis. Observing human-object interactions: Using spatial and functional compatibil-ity for recognition. In PAMI, 2009
20. B. Hixon, P. Clark, and H. Hajishirzi. Learning knowledge graphs for question answering through conversational dialog. In NAACL, 2015
21. M. J. Hosseini, H. Hajishirzi, O. Etzioni, and N. Kushman. Learning to solve arithmetic word problems with verb cate-gorization. In EMNLP, 2014
22. J. Huang et al. Exploring web scale language models for search query processing. In WWW, 2010
23. H. Izadinia, F. Sadeghi, and A. Farhadi. Incorporating scene context and object layout into appearance modeling. In CVPR, 2014
24. T. Lan et al. From subcategories to visual composites: A multi-level framework for object detection. In ICCV, 2013
25. T. Malisiewicz and A. A. Efros. Beyond categories: The vi-sual memex model for reasoning about object relationships. In NIPS, 2009
26. J.-B. Michel et al. Quantitative analysis of culture using mil-lions of digitized books. In Science, 2010
27. M. Pandey and S. Lazebnik. Scene recognition and weakly supervised object localization with deformable part-based models. In ICCV, 2011
28. J. Pearl. Probabilistic Reasoning in Intelligent Systems: Net-works of Plausible Inference. Morgan Kaufmann Publishers Inc., 1988
29. A. Ritter, Mausam, and O. Etzioni. A latent dirichlet alloca-tion method for selectional preferences. In ACL, 2010
30. O. Russakovsky et al. Detecting avocados to zucchinis: what have we done, and where are we going? In ICCV, 2013
31. F. Sadeghi and M. F. Tappen. Latent pyramidal regions for recognizing scenes. In ECCV, 2012
32. M. Sadeghi and A. Farhadi. Recognition using visual phrases. In CVPR, 2011
33. M. J. Seo, H. Hajishirzi, A. Farhadi, and O. Etzioni. Diagram understanding in geometry questions. In AAAI, 2014
34. R. Speer and C. Havasi. ConceptNet 5: A large semantic network for relational knowledge. In http://conceptnet5. media. mit. edu, 2013
35. E. Sudderth, A. Torralba, W. T. Freeman, and A. Wilsky. Learning hierarchical models of scenes, objects, and parts. In Proc. ICCV, 2005
36. P. Talukdar, D. T. Wijaya, and T. M. Mitchell. Acquiring temporal constraints between relations. In CIKM, 2012
37. L. von Ahn and L. Dabbish. Labeling images with a com-puter game. In SIGCHI, 2004
38. J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba. Sun database: Large-scale scene recognition from abbey to zoo. In CVPR, 2010
39. B. Yao and L. Fei-Fei. Modeling mutual context of object and human pose in human-object interaction activities. In CVPR, 2010
40. Y. Zhu, A. Fathi, and L. Fei-Fei. Reasoning about object affordances in a knowledge base representation. In ECCV, 2014
41. C. Zitnick, R. Vedantam, and D. Parikh. Adopting abstract images for semantic scene understanding. PAMI, 2014.