An empirical study and analysis of generalized zero-shot learning for object recognition in the wild

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9906 LNCS, Issue , 2016, Pages 52-68

  Chao, Wei Lun ^a   Changpinyo, Soravit ^a   Gong, Boqing ^b   Sha, Fei ^c  

^a University of Southern California ^*  (United States)

^b UNIVERSITY OF CENTRAL FLORIDA   (United States)

^c University of California   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

OBJECT RECOGNITION; SEMANTICS;

BENCHMARK DATASETS; CALIBRATION FACTORS; COMPARATIVE STUDIES; EMPIRICAL STUDIES; LEARNING METHODS; PERFORMANCE LIMITS; PERFORMANCE METRICES; VISUAL FEATURE;

COMPUTER VISION;

EID: 84990852341     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-46475-6_4     Document Type: Conference Paper

References (45)

1. Sudderth, E.B., Jordan, M.I.: Shared segmentation of natural scenes using dependent Pitman-Yor processes. In: NIPS (2008)
2. Salakhutdinov, R., Torralba, A., Tenenbaum, J.: Learning to share visual appearance for multiclass object detection. In: CVPR (2011)
3. Zhu, X., Anguelov, D., Ramanan, D.: Capturing long-tail distributions of object subcategories. In: CVPR (2014)
4. Lampert, C.H., Nickisch, H., Harmeling, S.: Learning to detect unseen object classes by between-class attribute transfer. In: CVPR (2009)
5. Farhadi, A., Endres, I., Hoiem, D., Forsyth, D.: Describing objects by their attributes. In: CVPR (2009)
6. Parikh, D., Grauman, K.: Relative attributes. In: ICCV (2011)
7. Mikolov, T., Chen, K., Corrado, G.S., Dean, J.: Efficient estimation of word representations in vector space. In: ICLR Workshops (2013)
8. Frome, A., Corrado, G.S., Shlens, J., Bengio, S., Dean, J., Ranzato, M., Mikolov, T.: Devise: A deep visual-semantic embedding model. In: NIPS (2013)
9. Socher, R., Ganjoo, M., Manning, C.D., Ng, A.: Zero-shot learning through crossmodal transfer. In: NIPS (2013)
10. Palatucci, M., Pomerleau, D., Hinton, G.E., Mitchell, T.M.: Zero-shot learning with semantic output codes. In: NIPS (2009)
11. Yu, X., Aloimonos, Y.: Attribute-based transfer learning for object categorization with zero/one training example. In: Maragos, P., Paragios, N., Daniilidis, K. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 127-140. Springer, Heidelberg (2010)
12. Rohrbach, M., Stark, M., Schiele, B.: Evaluating knowledge transfer and zero-shot learning in a large-scale setting. In: CVPR (2011)
13. Kankuekul, P., Kawewong, A., Tangruamsub, S., Hasegawa, O.: Online incremental attribute-based zero-shot learning. In: CVPR (2012)
14. Akata, Z., Perronnin, F., Harchaoui, Z., Schmid, C.: Label-embedding for attributebased classification. In: CVPR (2013)
15. Yu, F.X., Cao, L., Feris, R.S., Smith, J.R., Chang, S.F.: Designing category-level attributes for discriminative visual recognition. In: CVPR (2013)
16. Mensink, T., Gavves, E., Snoek, C.G.: Costa: Co-occurrence statistics for zero-shot classification. In: CVPR (2014)
17. Norouzi, M., Mikolov, T., Bengio, S., Singer, Y., Shlens, J., Frome, A., Corrado, G.S., Dean, J.: Zero-shot learning by convex combination of semantic embeddings. In: ICLR (2014)
18. Jayaraman, D., Grauman, K.: Zero-shot recognition with unreliable attributes. In:NIPS (2014)
19. Al-Halah, Z., Stiefelhagen, R.: How to transfer? Zero-shot object recognition via hierarchical transfer of semantic attributes. In: WACV (2015)
20. Akata, Z., Reed, S., Walter, D., Lee, H., Schiele, B.: Evaluation of output embeddings for fine-grained image classification. In: CVPR (2015)
21. Fu, Y., Hospedales, T.M., Xiang, T., Gong, S.: Transductive multi-view zero-shot learning. TPAMI 37, 2332-2345 (2015)
22. Fu, Z., Xiang, T., Kodirov, E., Gong, S.: Zero-shot object recognition by semantic manifold distance. In: CVPR (2015)
23. Li, X., Guo, Y., Schuurmans, D.: Semi-supervised zero-shot classification with label representation learning. In: ICCV (2015)
24. Romera-Paredes, B., Torr, P.H.S.: An embarrassingly simple approach to zero-shot learning. In: ICML (2015)
25. Kodirov, E., Xiang, T., Fu, Z., Gong, S.: Unsupervised domain adaptation for zero-shot learning. In: ICCV (2015)
26. Zhang, Z., Saligrama, V.: Zero-shot learning via semantic similarity embedding. In: ICCV (2015)
27. Zhang, Z., Saligrama, V.: Zero-shot learning via joint latent similarity embedding. In: CVPR (2016)
28. Changpinyo, S., Chao, W.L., Gong, B., Sha, F.: Synthesized classifiers for zero-shot learning. In: CVPR (2016)
29. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR (2009)
30. Mensink, T., Verbeek, J., Perronnin, F., Csurka, G.: Metric learning for large scale image classification: Generalizing to new classes at near-zero cost. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 488-501. Springer, Heidelberg (2012)
31. Tang, K.D., Tappen, M.F., Sukthankar, R., Lampert, C.H.: Optimizing one-shot recognition with micro-set learning. In: CVPR (2010)
32. Gan, C., Yang, Y., Zhu, L., Zhao, D., Zhuang, Y.: Recognizing an action using its name: A knowledge-based approach. IJCV 120, 1-17 (2016)
33. Elhoseiny, M., Saleh, B., Elgammal, A.: Write a classifier: zero-shot learning using purely textual descriptions. In: ICCV (2013)
34. Lei Ba, J., Swersky, K., Fidler, S., Salakhutdinov, R.: Predicting deep zero-shot convolutional neural networks using textual descriptions. In: ICCV (2015)
35. Scheirer, W.J., de Rezende Rocha, A., Sapkota, A., Boult, T.E.: Toward open set recognition. TPAMI 35(7), 1757-1772 (2013)
36. Scheirer, W.J., Jain, L.P., Boult, T.E.: Probability models for open set recognition. TPAMI 36(11), 2317-2324 (2014)
37. Jain, L.P., Scheirer, W.J., Boult, T.E.: Multi-class open set recognition using probability of inclusion. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part III. LNCS, vol. 8691, pp. 393-409. Springer, Heidelberg (2014)
38. Lampert, C.H., Nickisch, H., Harmeling, S.: Attribute-based classification for zeroshot visual object categorization. TPAMI 36(3), 453-465 (2014)
39. Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The Caltech-UCSD Birds-200-2011 dataset. Technical report CNS-TR-2011-001, California Institute of Technology (2011)
40. Kriegel, H.P., Kröger, P., Schubert, E., Zimek, A.: LoOP: Local outlier probabilities. In: CIKM (2009)
41. 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), 211-252 (2015)
42. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS (2013)
43. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: CVPR (2015)
44. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding. In: ACM Multimedia (2014)
45. Crammer, K., Singer, Y.: On the algorithmic implementation of multiclass kernelbased vector machines. JMLR 2, 265-292 (2002)