2020
DOI: 10.1007/978-3-030-51310-8_24
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Jointly Linking Visual and Textual Entity Mentions with Background Knowledge

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Cited by 4 publications
(2 citation statements)
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“…Feed-forward neural network (FFNN) techniques in the literature have been used for diagnosis in PV systems [37,[61][62][63][64]. The authors in [37] show an accuracy rate of 97.2% by predicting power output drops caused by short open-circuit faults, arrays and string degradation, and shadowing.…”
Section: Feed-forward Neural Networkmentioning
confidence: 99%
“…Feed-forward neural network (FFNN) techniques in the literature have been used for diagnosis in PV systems [37,[61][62][63][64]. The authors in [37] show an accuracy rate of 97.2% by predicting power output drops caused by short open-circuit faults, arrays and string degradation, and shadowing.…”
Section: Feed-forward Neural Networkmentioning
confidence: 99%
“…With the aim of producing clarifying explanations on why a particular image caption model fails or succeeds, recent strategies make sure that the objects the captions talk about are indeed detected in the images [111,112]. Textual explanations [15,[113][114][115][116][117] can also contribute to make vision and language models more robust, in the sense of being more semantically grounded [118][119][120].…”
Section: Image Captioning Modelsmentioning
confidence: 99%