Reciprocal Attention Fusion for Visual Question Answering
Table 1: Comparison of the state-of-the-art methods with our single model performance on VQAv1.0 test-dev and test-standard server.
['Methods', 'Test-dev Y/N', 'Test-dev No.', 'Test-dev Other', 'Test-dev All', 'Test-standard Y/N', 'Test-standard No.', 'Test-standard Other', 'Test-standard All']
Remarkably, our model outperforms all other models in the overall accuracy. We report a significant performance boost of 1.2% on the test-dev set and 0.3% on the test-standard set. It is to be noted that using multiple ensembles and data augmentation with complementary training in Visual Genome QA pairs can increase the accuracy performance of the VQA models. For instance, MCB Fukui et al. , MUTAN Ben-Younes et al. et al. It is interesting to note that except for MFB (7) all other ensemble models are ∼1% less than our reported single model performance. We do not ensemble our model or use data augmentation with complementary dataset as it makes the best results irreproducible and most of the models in the literature do not adopt this strategy.