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In this notebook we show the application of cross-modal techniques to improve the categorization of scientific papers through content related to figures, using both the textual part (captions) and the visual part (figures, diagrams, images) jointly. To this purpose, we use several CNN models and execute some experiments, illustrating our approach. This deposit contains high quality versions of the images used in the analysis.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 2 |

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