Modulated Self-attention Convolutional Network for VQA
Table 1: Experiments run on a ResNet-34. Numbers following S (stages) and B (blocks) indicate where SA (self-attention) modules are put. Parameters count concerns only SA and are in millions (M).
['[BOLD] ResNet-34', '[BOLD] Eval set %', '[BOLD] #param']
guistic input a CNN augmented with self-attentionWe show encouraging relative improvements for future research in this direction. [CONTINUE] We empirically found that self-attention was the most efficient in the 3rd stage. [CONTINUE] We notice small improvements relative to the baseline showing that self-attention alone does improve the VQA task. [CONTINUE] We showed that it is possible to improve the feature extraction procedure for the VQA task by adding self-attention modules in the different ResNet blocks.