Learning object models from few examples

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 9837, Issue , 2016, Pages

  Misra, Ishan ^a   Wang, Yuxiong ^a   Hebert, Martial ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Ground robots; Learning; Object recognition

Indexed keywords

OBJECT RECOGNITION;

COMPUTER VISION SYSTEM; DYNAMIC INTERACTION; GROUND ROBOT; LEARNING; LEARNING OBJECT MODEL; ONLINE LEARNING; TRAINING SAMPLE; VISUAL QUERY;

COMPUTER VISION;

EID: 84987784595     PISSN: 0277786X     EISSN: 1996756X     Source Type: Conference Proceeding    
DOI: 10.1117/12.2231108     Document Type: Conference Paper

References (61)

1. Oh, S., Hoogs, A., Perera, A., Cuntoor, N., Chen, C.C., Lee, J.T., Mukherjee, S., Aggarwal, J.K., Lee, H., Davis, L., Swears, E., Wang, X., Ji, Q., Reddy, K., Shah, M., Vondrick, C., Pirsiavash, H., Ramanan, D., Yuen, J., Torralba, A., Song, B., Fong, A., Roy-Chowdhury, A., Desai, M.: A large-scale benchmark dataset for event recognition in surveillance video. In: CVPR. (2011)
2. Prest, A., Leistner, C., Civera, J., Schmid, C., Ferrari, V.: Learning object class detectors from weakly annotated video. In: CVPR. (2012)
3. Tang, K., Sukthankar, R., Yagnik, J., Fei-Fei, L.: Discriminative segment annotation in weakly labeled video. In: CVPR. (2013)
4. Liang, X., Liu, S., Wei, Y., Liu, L., Lin, L., Yan, S.: Computational baby learning. arXiv preprint (2014)
5. Malisiewicz, T., Gupta, A., Efros, A.A.: Ensemble of exemplar-svms for object detection and beyond. In: ICCV. (2011)
6. Hariharan, B., Malik, J., Ramanan, D.: Discriminative decorrelation for clustering and classification. In: ECCV. (2012)
7. Shrivastava, A., Malisiewicz, T., Gupta, A., Efros, A.A.: Data-driven visual similarity for cross-domain image matching. ACM Trans. on Graphics (2011)
8. Vondrick, C., Khosla, A., Malisiewicz, T., Torralba, A.: HOGgles: Visualizing Object Detection Features. ICCV (2013)
9. Rosenberg, C., Hebert, M., Schneiderman, H.: Semi-supervised self-training of object detection models. In: WACV. (2005)
10. Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: CVPR. (2007)
11. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR. (2005)
12. Kanamori, T., Hido, S., Sugiyama, M.: A least-squares approach to direct importance estimation. JMLR (2009)
13. Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Image Understanding Workshop. (1981)
14. Kalal, Z., Mikolajczyk, K., Matas, J.: Forward-backward error: Automatic detection of tracking failures. In: ICPR. (2010)
15. Berclaz, J., Fleuret, F., Turetken, E., Fua, P.: Multiple object tracking using k-shortest paths optimization. TPAMI (2011)
16. Pirsiavash, H., Ramanan, D., Fowlkes, C.C.: Globally-optimal greedy algorithms for tracking a variable number of objects. In: CVPR. (2011)
17. Van de Sande, K.E.A., Uijlings, J.R.R., Gevers, T., Smeulders, A.W.M.: Segmentation as selective search for object recognition. IJCV (2011)
18. Misra, I., Shrivastava, A., Hebert, M.: Data-driven exemplar model selection. In: WACV. (2014)
19. Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. PAMI (2010)
20. Pirsiavash, H., Ramanan, D., Fowlkes, C.C.: Globally-optimal greedy algorithms for tracking a variable number of objects. In: CVPR. (2011)
21. Wang, Y.X., Hebert, M.: Model recommendation: Generating object detectors from few samples. In: CVPR. (2015)
22. Wang, Y.X., Hebert, M.: Learning by transferring from unsupervised universal sources. In: AAAI. (2016)
23. Rastegari, M., Farhadi, A., Forsyth, D.: Attribute discovery via predictable discriminative binary codes. In: ECCV. (2012)
24. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS. (2012)
25. Malisiewicz, T., Gupta, A., Efros, A.A.: Ensemble of exemplar-svms for object detection and beyond. In: ICCV. (2011)
26. Dai, D., Gool, L.V.: Ensemble projection for semi-supervised image classification. In: ICCV. (2013)
27. Dai, D., Van Gool, L.: Ensemble projection with cnn features for semi-supervised image classification and image clustering. Technical report, ETH Zurich (May 2015)
28. Choi, J., Rastegari, M., Farhadi, A., Davis, L.S.: Adding unlabeled samples to categories by learned attributes. In: CVPR. (2013)
29. Matikainen, P., Sukthankar, R., Hebert, M.: Classifier ensemble recommendation. In: ECCV Workshop on Web-scale Vision and Social Media. (2012)
30. Matikainen, P., Sukthankar, R., Hebert, M.: Model recommendation for action recognition. In: CVPR. (2012)
31. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Computer 42(8) (2009)
32. Koren, Y.: Factor in the neighbors: Scalable and accurate collaborative filtering. TKDD 4(1) (2010) 1{24
33. Wang, Y.X., Zhang, Y.J.: Nonnegative matrix factorization: A comprehensive review. TKDE 25(6) (2013)
34. Pan, S.J., Yang, Q.: A survey on transfer learning. TKDE 22(10) (2010)
35. Kienzle, W., Chellapilla, K.: Personalized handwriting recognition via biased regularization. In: ICML. (2006)
36. Yang, J., Yan, R., Hauptmann, A.: Adapting SVM classifiers to data with shifted distributions. In: ICDM Workshops. (2007)
37. Yang, J., Yan, R., Hauptmann, A.: Cross-domain video concept detection using adaptive SVMs. In: ACM MM. (2007)
38. Yang, J., Hauptmann, A.G.: A framework for classifier adaptation and its applications in concept detection. In: MIR. (2008)
39. Duan, L., Tsang, I.W., Xu, D., Chua, T.S.: Domain adaptation from multiple sources via auxiliary classifiers. In: ICML. (2009)
40. Chattopadhyay, R., Ye, J., Panchanathan, S., Fan, W., Davidson, I.: Multisource domain adaptation and its application to early detection of fatigue. In: SIGKDD. (2011)
41. Aytar, Y., Zisserman, A.: Tabula rasa: Model transfer for object category detection. In: ICCV. (2011)
42. Aytar, Y., Zisserman, A.: Enhancing exemplar svms using part level transfer regularization. In: BMVC. (2012)
43. Kuzborskij, I., Orabona, F.: Stability and hypothesis transfer learning. In: ICML. (2013)
44. Kuzborskij, I., Orabona, F., Caputo, B.: From N to N+1: Multiclass transfer incremental learning. In: CVPR. (2013)
45. Tommasi, T., Orabona, F., Caputo, B.: Learning categories from few examples with multi model knowledge transfer. TPAMI 36(5) (2014)
46. Kuzborskij, I., Caputo, B., Orabona, F.: Transfer learning through greedy subset selection. In: ICIAP. (2015)
47. Kuzborskij, I., Orabona, F.: Fast rates by transferring from auxiliary hypotheses. arXiv:1412.1619 (2015)
48. Lee, H., Battle, A., Raina, R., Ng, A.: Efficient sparse coding algorithms. In: NIPS. (2006)
49. Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T.: Decaf: A deep convolutional activation feature for generic visual recognition. In: ICML. (2014)
50. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet Large Scale Visual Recognition Challenge. IJCV 115(3) (2015)
51. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR. (2014)
52. Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes (VOC) Challenge. IJCV 88(2) (2010)
53. Uijlings, J., van de Sande, K., Gevers, T., Smeulders, A.: Selective search for object recognition. IJCV 104(2) (2013)
54. Choi, M.J., Lim, J.J., Torralba, A., Willsky, A.S.: Exploiting hierarchical context on a large database of object categories. In: Computer vision and pattern recognition (CVPR), 2010 IEEE conference on, IEEE (2010)
55. Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. TPAMI 32(9) (2010)
56. Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: ECCV. (2010)
57. Hoffman, J., Tzeng, E., Donahue, J., Jia, Y., Saenko, K., Darrell, T.: One-shot adaptation of supervised deep convolutional models. In: ICLR. (2014)
58. Gong, B., Shi, Y., Sha, F., Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: CVPR. (2012)
59. Fernando, B., Habrard, A., Sebban, M., Tuytelaars, T.: Unsupervised visual domain adaptation using subspace alignment. In: ICCV. (2013)
60. Daume III, H.: Frustratingly easy domain adaptation. In: ACL. (2007)
61. Hoffman, J., Rodner, E., Donahue, J., Darrell, T., Saenko, K.: Efficient learning of domain-invariant image representations. In: ICLR. (2013)