Show, Adapt and Tell: Adversarial Training of Cross-Domain Image Captioner
- Published: 02 May 2017
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
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doi: 10.1109/iccv.2017.64
[1] H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, and M. Marchand. Domain-adversarial neural networks. In NIPS workshop on Transfer and Multi-Task Learning: Theory meets Practice, 2014. 2
[2] P. Anderson, B. Fernando, M. Johnson, and S. Gould. Guided open vocabulary image captioning with constrained beam search. CoRR, abs/1612.00576, 2016. 2
[3] S. Antol, A. Agrawal, J. Lu, M. Mitchell, D. Batra, C. Lawrence Zitnick, and D. Parikh. Vqa: Visual question answering. In ICCV, 2015. 5
[4] D. Bahdanau, P. Brakel, K. Xu, A. Goyal, R. Lowe, J. Pineau, A. Courville, and Y. Bengio. An actor-critic algorithm for sequence prediction. In ICLR, 2017. 2
[5] S. Bengio, O. Vinyals, N. Jaitly, and N. Shazeer. Scheduled sampling for sequence prediction with recurrent neural networks. In NIPS, 2015. 2, 6
[6] G. Coppersmith and E. Kelly. Dynamic wordclouds and vennclouds for exploratory data analysis. In Workshop on Interactive Language Learning, Visualization, and Interfaces. 7
[7] 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. 2
[8] J. Donahue, L. Anne Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell. Long-term recurrent convolutional networks for visual recognition and description. In CVPR, 2015. 1, 2
[9] Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky. Domain-adversarial training of neural networks. JMLR, 17(59):1-35, 2016. 2
[10] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial nets. In Advances in neural information processing systems, pages 2672-2680, 2014. 2
[11] A. Goyal, A. Lamb, Y. Zhang, S. Zhang, A. C. Courville, and Y. Bengio. Professor forcing: A new algorithm for training recurrent networks. In NIPS, 2016. 2
[12] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR, 2016. 6
[13] L. A. Hendricks, Z. Akata, M. Rohrbach, J. Donahue, B. Schiele, and T. Darrell. Generating visual explanations. In ECCV, 2016. 2
[14] L. A. Hendricks, S. Venugopalan, M. Rohrbach, R. Mooney, S. Kate, and T. Darrell. Deep compositional captioning: Describing novel object categories without paired training data. In CVPR, 2016. 2, 7
[15] J. Hoffman, D. Wang, F. Yu, and T. Darrell. Fcns in the wild: Pixel-level adversarial and constraint-based adaptation. CoRR, abs/1612.02649, 2016. 3
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doi: 10.1109/iccv.2017.64
[1] H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, and M. Marchand. Domain-adversarial neural networks. In NIPS workshop on Transfer and Multi-Task Learning: Theory meets Practice, 2014. 2
[2] P. Anderson, B. Fernando, M. Johnson, and S. Gould. Guided open vocabulary image captioning with constrained beam search. CoRR, abs/1612.00576, 2016. 2
[3] S. Antol, A. Agrawal, J. Lu, M. Mitchell, D. Batra, C. Lawrence Zitnick, and D. Parikh. Vqa: Visual question answering. In ICCV, 2015. 5
[4] D. Bahdanau, P. Brakel, K. Xu, A. Goyal, R. Lowe, J. Pineau, A. Courville, and Y. Bengio. An actor-critic algorithm for sequence prediction. In ICLR, 2017. 2
[5] S. Bengio, O. Vinyals, N. Jaitly, and N. Shazeer. Scheduled sampling for sequence prediction with recurrent neural networks. In NIPS, 2015. 2, 6
[6] G. Coppersmith and E. Kelly. Dynamic wordclouds and vennclouds for exploratory data analysis. In Workshop on Interactive Language Learning, Visualization, and Interfaces. 7
[7] 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. 2
[8] J. Donahue, L. Anne Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell. Long-term recurrent convolutional networks for visual recognition and description. In CVPR, 2015. 1, 2
[9] Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky. Domain-adversarial training of neural networks. JMLR, 17(59):1-35, 2016. 2
[10] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial nets. In Advances in neural information processing systems, pages 2672-2680, 2014. 2
[11] A. Goyal, A. Lamb, Y. Zhang, S. Zhang, A. C. Courville, and Y. Bengio. Professor forcing: A new algorithm for training recurrent networks. In NIPS, 2016. 2
[12] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR, 2016. 6
[13] L. A. Hendricks, Z. Akata, M. Rohrbach, J. Donahue, B. Schiele, and T. Darrell. Generating visual explanations. In ECCV, 2016. 2
[14] L. A. Hendricks, S. Venugopalan, M. Rohrbach, R. Mooney, S. Kate, and T. Darrell. Deep compositional captioning: Describing novel object categories without paired training data. In CVPR, 2016. 2, 7
[15] J. Hoffman, D. Wang, F. Yu, and T. Darrell. Fcns in the wild: Pixel-level adversarial and constraint-based adaptation. CoRR, abs/1612.02649, 2016. 3
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