Visual Question Answering with Memory-Augmented Networks
Table 1: Single model accuracy on the VQA benchmark [2]. The proposed algorithm performs favorably against state-of-the-art methods in both the multiple-choice and open-ended tasks. The best and second best values are highlighted by bold and underline.
['[EMPTY]', 'Test-dev Multiple-choice', 'Test-dev Multiple-choice', 'Test-dev Multiple-choice', 'Test-dev Multiple-choice', 'Test-dev Open-ended', 'Test-dev Open-ended', 'Test-dev Open-ended', 'Test-dev Open-ended', 'Test-standard Multiple-choice', 'Test-standard Multiple-choice', 'Test-standard Multiple-choice', 'Test-standard Multiple-choice', 'Test-standard Open-ended', 'Test-standard Open-ended', 'Test-standard Open-ended', 'Test-standard Open-ended']
For fairness, we compare with single model approaches on the VQA benchmark leader-board, and highlight the methods using the ResNet The proposed method performs favorably against state of the art in both the open-ended and multiple-choice tasks. It is worth mentioning that even with VGGNet features, our method generally performs well against the state-of-the-art algorithms with ResNet features. However, it does not incorporate an augmented external memory and thus does not make full use of scarce training exemplars. With the use of memory-augmented networks and sequential co-attention mechanism, our method reaches substantially higher accuracy than the HieCoAtt and DMN+ methods in all test settings: The performance gains in the test-dev validation are 3.7% (multiple-choice) and 2.0% (open-ended) compared to HieCoAtt