Few-shot object recognition from machine-labeled web images

Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017

Volumn 2017-January, Issue , 2017, Pages 5358-5366

  Xu, Zhongwen ^a   Zhu, Linchao ^a   Yang, Yi ^a  

^a UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ABSTRACTING; COMPUTER VISION; DATA VISUALIZATION; IMAGE ANALYSIS; IMAGE ANNOTATION; OBJECT RECOGNITION;

ABUNDANT RESOURCES; ATTENTION MECHANISMS; CONVOLUTIONAL NEURAL NETWORK; EXTERNAL MEMORY; IMAGE FEATURES; MEMORY BANKS; MEMORY SLOTS; SIMILAR DATUM;

LONG SHORT-TERM MEMORY;

EID: 85044454030     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2017.569     Document Type: Conference Paper

References (39)

1. M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng. TensorFlow: A system for large-scale machine learning. In OSDI. USENIX Association, 2016.
2. J. L. Ba, J. R. Kiros, and G. E. Hinton. Layer normalization. arXiv preprint arXiv:1607.06450 2016.
3. D. Bahdanau, K. Cho, and Y. Bengio. Neural machine translation by jointly learning to align and translate. In ICLR, 2015.
4. Y. Bengio, P. Simard, and P. Frasconi. Learning longterm dependencies with gradient descent is difficult. TNN, 5(2):157-166, 1994.
5. C. M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
6. X. Chen, A. Shrivastava, and A. Gupta. NEIL: Extracting visual knowledge from web data. In CVPR, 2013.
7. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. FeiFei. ImageNet: A large-scale hierarchical image database. In CVPR.
8. S. K. Divvala, A. Farhadi, and C. Guestrin. Learning everything about anything: Webly-supervised visual concept learning. In CVPR, 2014.
9. L. Fei-Fei, R. Fergus, and P. Perona. One-shot learning of object categories. TPAMI, 28(4):594-611, 2006.
10. X. Glorot and Y. Bengio. Understanding the difficulty of training deep feedforward neural networks. In AISTATS, 2010.
11. A. Graves, G. Wayne, and I. Danihelka. Neural Turing machines. arXiv preprint arXiv:1410.5401 2014.
12. K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR, 2016.
13. K. He, X. Zhang, S. Ren, and J. Sun. Identity mappings in deep residual networks. In ECCV, 2016.
14. S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural Computation, 9(8):1735-1780, 1997.
15. A. Joulin, L. van der Maaten, A. Jabri, and N. Vasilache. Learning visual features from large weakly supervised data. In ECCV, 2016.
16. R. Jozefowicz, O. Vinyals, M. Schuster, N. Shazeer, and Y. Wu. Exploring the limits of language modeling. arXiv preprint arXiv:1602.02410 2016.
17. A. Karpathy and L. Fei-Fei. Deep visual-semantic alignments for generating image descriptions. In CVPR, 2015.
18. D. Kingma and J. Ba. Adam: A method for stochastic optimization. In ICLR, 2015.
19. I. Krasin, T. Duerig, N. Alldrin, A. Veit, S. Abu-El-Haija, S. Belongie, D. Cai, Z. Feng, V. Ferrari, V. Gomes, A. Gupta, D. Narayanan, C. Sun, G. Chechik, and K. Murphy. OpenImages: A public dataset for large-scale multi-label and multiclass image classification. Dataset available from https://github.com/openimages, 2016.
20. B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum. Humanlevel concept learning through probabilistic program induction. Science, 350(6266):1332-1338, 2015.
21. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998.
22. T. Malisiewicz, A. Gupta, and A. A. Efros. Ensemble of exemplar-SVMs for object detection and beyond. In ICCV, 2011.
23. T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In NIPS, 2013.
24. A. Miller, A. Fisch, J. Dodge, A.-H. Karimi, A. Bordes, and J. Weston. Key-value memory networks for directly reading documents. EMNLP, 2016.
25. P. Pan, Z. Xu, Y. Yang, F. Wu, and Y. Zhuang. Hierarchical recurrent neural encoder for video representation with application to captioning. In CVPR, 2016.
26. A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap. One-shot learning with memory-augmented neural networks. In ICML, 2016.
27. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015.
28. S. Sukhbaatar, A. Szlam, J. Weston, and R. Fergus. End-to-end memory networks. In NIPS, 2015.
29. I. Sutskever, O. Vinyals, and Q. V. Le. Sequence to sequence learning with neural networks. In NIPS, 2014.
30. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In CVPR, 2015.
31. S. Venugopalan, M. Rohrbach, J. Donahue, R. Mooney, T. Darrell, and K. Saenko. Sequence to sequence-video to text. In ICCV, 2015.
32. O. Vinyals, S. Bengio, and M. Kudlur. Order matters: Sequence to sequence for sets. In ICLR, 2016.
33. O. Vinyals, C. Blundell, T. Lillicrap, K. Kavukcuoglu, and D. Wierstra. Matching networks for one shot learning. In NIPS, 2016.
34. O. Vinyals, A. Toshev, S. Bengio, and D. Erhan. Show and tell: A neural image caption generator. In CVPR, 2015.
35. J. Weston, A. Bordes, S. Chopra, A. M. Rush, B. van Merriënboer, A. Joulin, and T. Mikolov. Towards AI-complete question answering: A set of prerequisite toy tasks. arXiv preprint arXiv:1502.05698 2015.
36. J. Weston, S. Chopra, and A. Bordes. Memory networks. In ICLR, 2015.
37. K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, and Y. Bengio. Show, attend and tell: Neural image caption generation with visual attention. In ICML, 2015.
38. J. Yue-Hei Ng, M. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. Toderici. Beyond short snippets: Deep networks for video classification. In CVPR, 2015.
39. W. Zaremba, I. Sutskever, and O. Vinyals. Recurrent neural network regularization. arXiv preprint arXiv:1409.2329 2014.