Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering
Table 1: Accuracy results on the DAQUAR dataset (in percentage).
['[EMPTY]', 'DAQUAR']
Results of our SMem-VQA model on the DAQUAR dataset and the baseline model results reported in previous work are shown in Tab. From the DAQUAR result in Tab. Modeling the question only with either the LSTM model or Question BOW model does equally well in comparison, indicating the the question text contains important prior information for predicting the answer. Also, on this dataset, the VIS+LSTM model achieves better accuracy than Neural-Image-QA model; the former shows the image only at the first timestep of the LSTM, while the latter does so at each timestep. In comparison, both our One-Hop model and Two-Hop spatial attention models outperform the IMG+BOW, as well as the other baseline models. A major advantage of our model is the ability to visualize the inference process in the deep network. To illustrate this, two attention weights visualization examples in SMem-VQA One-Hop and Two-Hop models on DAQUAR dataset are shown in Fig.