Deep Multimodal Neural Architecture Search
Table 3. Accuracies on the test-dev and test-std splits of VQA-v2 to compare with the state of the arts approaches. All methods use the same visual features (Anderson et al., 2018) and are trained on the train+val+vg splits, where vg denotes the augmented dataset from Visual Genome.
['Method', 'Size', 'Dev All', 'Dev Y/N', 'Dev Num', 'Dev Other', 'Std All']
The demonstrated results show that: 1) with 1/4∼1/3 model size, MMnasNet (0.5×) model achieves competitive performance to the previous state-of-the-art models; and 2) with nearly the same model size, MMnasNet (1×) outperforms existing top performance approaches by a clear margin on all answer types.