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There is considerable interest in the task of automatically generating image captions.
A probabilistic Earley parser as a psycholinguistic model
Hale, J.: · 2001
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Bleu: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., Zhu, W.: · 2002
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Accurate unlexicalized parsing
Klein, D., Manning, C.D.: · 2003
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ROUGE: A package for automatic evaluation of summaries
Lin, C.Y.: · 2004
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Shallow semantic parsing using support vector machines
Pradhan, S.S., Ward, W., Hacioglu, K., Martin, J.H., Jurafsky, D.: · 2004
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Expectation-based syntactic comprehension
Levy, R.: · 2008
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Collecting image annotations using Amazon’s Mechanical Turk
Rashtchian, C., Young, P., Hodosh, M., Hockenmaier, J.: · 2010
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Unbiased look at dataset bias
Torralba, A., Efros, A.A.: · 2011
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Fully automatic semantic MT evaluation
Lo, C.k., Tumuluru, A.K., Wu, D.: · 2012
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Abstract meaning representation (AMR) 1.0 specification
Banarescu, L., Bonial, C., Cai, S., Georgescu, M., Griffitt, K., Hermjakob, U., Knight, K., Koehn, P., Palmer, M., Schneider, N.: · 2012
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Framing image description as a ranking task: Data, models and evaluation metrics
Hodosh, M., Young, P., Hockenmaier, J.: · 2013
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BabyTalk: Understanding and generating simple image descriptions
Kulkarni, G., Premraj, V., Ordonez, V., Dhar, S., Li, S., Choi, Y., Berg, A.C., Berg, T.L.: · 2013
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Smatch: an evaluation metric for semantic feature structures
Cai, S., Knight, K.: · 2013
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From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
Young, P., Lai, A., Hodosh, M., Hockenmaier, J.: · 2014
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Microsoft COCO: Common objects in context
Lin, T., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollar, P., Zitnick, C.L.: · 2014
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Comparing automatic evaluation measures for image description
Elliott, D., Keller, F.: · 2014
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Meteor universal: Language specific translation evaluation for any target language
Denkowski, M., Lavie, A.: · 2014
Cited alongside, same era.
Visual semantic search: Retrieving videos via complex textual queries
Lin, D., Fidler, S., Kong, C., Urtasun, R.: · 2014
Cited alongside, same era.
Universal stanford dependencies: A cross-linguistic typology
De Marneffe, M.C., Dozat, T., Silveira, N., Haverinen, K., Ginter, F., Nivre, J., Manning, C.D.: · 2014
Cited alongside, same era.
A discriminative graph-based parser for the abstract meaning representation
Flanigan, J., Thomson, S., Carbonell, J., Dyer, C., Smith, N.A.: · 2014
Cited alongside, same era.
Results of the WMT14 metrics shared task
Machacek, M., Bojar, O.: · 2014
Semantic tuples for evaluation of image sentence generation
Ellebracht, L., Ramisa, A., Swaroop, P., Cordero, J., Moreno-Noguer, F., Quattoni., A.: · 2015
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Robust subgraph generation improves abstract meaning representation parsing
Werling, K., Angeli, G., Manning, C.: · 2015
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Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models
Plummer, B.A., Wang, L., Cervantes, C.M., Caicedo, J.C., Hockenmaier, J., Lazebnik, S.: · 2015
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Results of the WMT15 metrics shared task
Stanojević, M., Kamran, A., Koehn, P., Bojar, O.: · 2015
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From images to sentences through scene description graphs using commonsense reasoning and knowledge
Aditya, S., Yang, Y., Baral, C., Fermuller, C., Aloimonos, Y.: · 2015
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Cited alongside, same era.
Deep captioning with multimodal recurrent neural networks (m-rnn)
Mao, J., Xu, W., Yang, Y., Wang, J., Huang, Z., Yuille, A.: · 2014
Cited alongside, same era.
Multimodal neural language models
Kiros, R., Salakhutdinov, R., Zemel, R.S.: · 2014
Cited alongside, same era.
Long-term recurrent convolutional networks for visual recognition and description
Donahue, J., Hendricks, L.A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., Darrell, T.: · 2015
Cited alongside, same era.
Show, attend and tell: Neural image caption generation with visual attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A.C., Salakhutdinov, R., Zemel, R.S., Bengio, Y.: · 2015
Cited alongside, same era.
Microsoft COCO captions: Data collection and evaluation server
Chen, X., Fang, H., Lin, T.Y., Vedantam, R., Gupta, S., Dollar, P., Zitnick, C.L.: · 2015
Cited alongside, same era.
CIDEr: Consensus-based image description evaluation
Vedantam, R., Zitnick, C.L., Parikh, D.: · 2015
Cited alongside, same era.
Deep visual-semantic alignments for generating image descriptions
Karpathy, A., Fei-Fei, L.: · 2015
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From captions to visual concepts and back
Fang, H., Gupta, S., Iandola, F.N., Srivastava, R., Deng, L., Dollar, P., Gao, J., He, X., Mitchell, M., Platt, J.C., Zitnick, C.L., Zweig, G.: · 2015
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Show and tell: A neural image caption generator
Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: · 2015
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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., Mitchell, M.: · 2015
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Learning like a child: Fast novel visual concept learning from sentence descriptions of images
Mao, J., Wei, X., Yang, Y., Wang, J., Huang, Z., Yuille, A.L.: · 2015
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Exploring nearest neighbor approaches for image captioning
Devlin, J., Gupta, S., Girshick, R.B., Mitchell, M., Zitnick, C.L.: · 2015
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Technical report: Image captioning with semantically similar images
Kolár, M., Hradis, M., Zemcík, P.: · 2015
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Automatic description generation from images: A survey of models, datasets, and evaluation measures
Bernardi, R., Cakici, R., Elliott, D., Erdem, A., Erdem, E., Ikizler-Cinbis, N., Keller, F., Muscat, A., Plank, B.: · 2016
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Visual genome: Connecting language and vision using crowdsourced dense image annotations
Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.J., Shamma, D.A., Bernstein, M., Fei-Fei, L.: · 2016
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