Abstract
The world around us is composed of images that often need to be translated into words. This translation can take place in parts, converting regions of the image into textual descriptions what is also known as dense captioning. By doing so, the information present in this region is converted into words expressing the way objects relate to each other. Computational models have been proposed to perform this task in a similar way to human beings, mainly using deep neural networks. As the same region of the image can be described in several different forms, this study proposes to use the Abstract Meaning Representation (AMR) in the generation of descriptions for a given region. We hypothesize that by using AMR it would be possible to extract the meaning of the text and, as a consequence, improve the quality of the sentences produced by the models. AMR was investigated as a semantic representation formalism evolving the so far proposed models that are based only on purely natural language. The results show that the models trained with sentences in the form of AMR led to better descriptions and the performance achieved was superior in almost all evaluations.
Keywords
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Image from Visual Genome Dataset [10].
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The success of syntactic banks is due to the fact that unifying the various tasks in a single process allowed the use of a single tool. An example of a classic syntactic bank is the Penn Treebank.
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The value before the slash indicates the total of unique tokens (types) and the value after the slash, the total number of occurrences of those tokens.
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Acknowledgements
This study was funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Funding Code 001. The first author was funded by the grant #2018/1771510, CAPES. This research is also part of the MMeaning project, supported by São Paulo Research Foundation (FAPESP), grant #2016/13002-0.
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Neto, A.M.S.A., Caseli, H.M., Almeida, T.A. (2020). Dense Captioning Using Abstract Meaning Representation. In: Cerri, R., Prati, R.C. (eds) Intelligent Systems. BRACIS 2020. Lecture Notes in Computer Science(), vol 12319. Springer, Cham. https://doi.org/10.1007/978-3-030-61377-8_31
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