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When designing a neural caption generator, a convolutional neural network can be used to extract image features.
Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication
Jaeger, H. and Haas, H. (2004) · 2004
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METEOR: An automatic metric for MT evaluation with improved correlation with human judgments
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Understanding the difficulty of training deep feedforward neural networks
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A survey on transfer learning
Pan, S. J. and Yang, Q. (2010) · 2010
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012) · 2012
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Adadelta: An adaptive learning rate method
Zeiler, M. D. (2012) · 2012
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Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics
Hodosh, M., Young, P., and Hockenmaier, J. (2013) · 2013
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Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013) · 2013
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Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, J., Gülçehre, Ç., Cho, K., and Bengio, Y. (2014) · 2014
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Microsoft COCO: Common objects in context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L. (2014) · 2014
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Explain images with multimodal recurrent neural networks
Mao, J., Xu, W., Yang, Y., Wang, J., and Yuille, A. L. (2014) · 2014
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Adam: A Method for Stochastic Optimization
P. Kingma, D. and Ba, J. (2014) · 2014
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Glove: Global vectors for word representation
Pennington, J., Socher, R., and Manning, C. (2014) · 2014
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K. and Zisserman, A. (2014) · 2014
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How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014) · 2014
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Language models for image captioning: The quirks and what works
Devlin, J., Cheng, H., Fang, H., Gupta, S., Deng, L., He, X., Zweig, G., and Mitchell, M. (2015) · 2015
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Image representations and new domains in neural image captioning
Hessel, J., Savva, N., and Wilber, M. J. (2015) · 2015
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Spice: Semantic propositional image caption evaluation
Anderson, P., Fernando, B., Johnson, M., and Gould, S. (2016) · 2016
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Deep Compositional Captioning: Describing Novel Object Categories without Paired Training Data
Hendricks, L. A., Venugopalan, S., Rohrbach, M., Mooney, R., Saenko, K., and Darrell, T. (2016) · 2016
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Optimization of image description metrics using policy gradient methods
Liu, S., Zhu, Z., Ye, N., Guadarrama, S., and Murphy, K. (2016) · 2016
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How transferable are neural networks in nlp applications?
Mou, L., Meng, Z., Yan, R., Li, G., Xu, Y., Zhang, L., and Jin, Z. (2016) · 2016
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Transfer learning for low-resource neural machine translation
Zoph, B., Yuret, D., May, J., and Knight, K. (2016) · 2016
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Re-evaluating automatic metrics for image captioning
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Deep Visual-Semantic Alignments for Generating Image Descriptions
Karpathy, A. and Fei-Fei, L. (2015) · 2015
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From word embeddings to document distances
Kusner, M., Sun, Y., Kolkin, N., and Weinberger, K. (2015) · 2015
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CIDEr: Consensus-based image description evaluation
Vedantam, R., Zitnick, C. L., and Parikh, D. (2015) · 2015
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Show and tell: A neural image caption generator
Vinyals, O., Toshev, A., Bengio, S., and Erhan, D. (2015) · 2015
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Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)
Mao, J., Xu, W., Yang, Y., Wang, J., Huang, Z., and Yuille, A. (2015a)
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Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images
Mao, J., Xu, W., Yang, Y., Wang, J., Huang, Z., and Yuille, A. (2015b)
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Kilickaya, M., Erdem, A., Ikizler-Cinbis, N., and Erdem, E. (2017) · 2017
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Unsupervised pretraining for sequence to sequence learning
Ramachandran, P., Liu, P., and Le, Q. (2017) · 2017
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Universal language model fine-tuning for text classification
Howard, J. and Ruder, S. (2018) · 2018
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Do better imagenet models transfer better?
Kornblith, S., Shlens, J., and Le, Q. V. (2018) · 2018
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Where to put the image in an image caption generator
Tanti, M., Gatt, A., and Camilleri, K. P. (2018) · 2018
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