Answering Questions about Data Visualizations using Efficient Bimodal Fusion
Table 7: Bar chart reconstruction accuracy (%) using Algorithm 1 with PreFIL (Oracle OCR).
['[EMPTY]', 'Test Familiar', 'Test Novel']
We introduce table reconstruction for DVQA as an application of PReFIL. DVQA’s question templates provide the questions needed to completely reconstruct its bar charts by iteratively asking questions about each chart. An example reconstruction is shown in Fig. Shape prediction can be done with near perfect accuracy, but there is a drop in performance for both label and value prediction. To study the accuracy of different components in chart reconstruction, we also report accuracy on three main components of the iterative question-answering: 1) Shape prediction: Questions about number of bars and legends in the picture; 2) Label prediction: Predicting the label of given bar or legend; and 3) Value Prediction: Predicting the value of a given bar.