Reciprocal Attention Fusion for Visual Question Answering
Table 2: Comparison of the state-of-the-art methods with our single model performance on VQAv2.0 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']
in all question categories and overall by a significant margin of 1.7%. The bottom up, adaptive-kTeney et al. et al. reports currently the best performance among on VQAv2 test-standard dataset. This indicates our models superior capability to interpret and incorporate multi-modal relationships for visual reasoning.