VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic Segmentation
Table 2: Comparison results on VQA TestDev Real Multiple Choice task.
['Method', 'Y/N', 'Num.', 'Others', 'All']
What is good supervision for attention in VQA? In this section, we contrast the VQS data to the human attention maps (HAT) We can see that the segmentaitons linked to QAs give rise to a little better results than bounding boxes, which further outperform HAT. These confirm our conjecture that HAT might be suboptimal for the supervised learning of attentions in VQA, since they reveal usually small parts of objects and contain large proportions of background.