Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering
Table 2: Test-dev and test-standard results on the Open-Ended VQA dataset (in percentage). Models with ∗ use external training data in addition to the VQA dataset.
['[EMPTY]', 'test-dev  [BOLD] Overall', 'test-dev yes/no', 'test-dev number', 'test-dev others', 'test-standard  [BOLD] Overall', 'test-standard yes/no', 'test-standard number', 'test-standard others']
The overall accuracy and per-answer category accuracy for our SMem-VQA models and the four baseline models on VQA dataset are shown in Tab. From the table, we can see that the SMem-VQA One-Hop model obtains slightly better results compared to the iBOWIMG model. However, the SMem-VQA Two-Hop model achieves an improvement of 2.27% on test-dev and 2.35% on test-standard compared to the iBOWIMG model, demonstrating the value of spatial attention. The SMem-VQA Two-Hop model also shows best performance in the per-answer category accuracy. The SMem-VQA Two-Hop model has slightly better result than the DPPnet model. The DPPnet model uses a large-scale text corpus to pre-train the Gated Recurrent Unit (GRU) network for question representation. Considering the fact that our model does not use extra data to pre-train the word embeddings, its results are very competitive. We also experiment with adding a third hop into our model on the VQA dataset, but the result does not improve further.