An Online Presentation Slide Assessment System Using Visual and Semantic Segmentation Features

Shengzhou Yi, Junichiro Matsugami, Hiroshi Yumoto, Toshihiko Yamasaki

Abstract: In this study, we present a new presentation slide assessment system that can extract the structural features from any slide file formats. Our previous work used a neural network to identify novice vs. well-designed presentation slides based on visual and structural features. However, the structural feature extraction was only applicable to PowerPoint files. To solve this problem, we extract the semantic segmentation from the slide images as a new format of structural features. The proposed multi-modal Transformer extracts the features from the original images and semantic segmentation results to assess the slide design. The prediction targets are the top-10 checkpoints pointed out by the professional consultants. Class-imbalanced learning and multi-task learning methods are also applied to improve the accuracy. The proposed model only requiring the slide images achieved an average accuracy of 81.67% that is comparative to the performance of the previous work requiring the PowerPoint files.