RUBi: Reducing Unimodal Biases for Visual Question Answering
Table 3: Overall accuracy of the RUBi learning strategy on VQA v2 val and test-dev splits.
['Model', 'val', 'test-dev']
S4SS1SSS0Px3 Architecture agnostic RUBi can be used on existing VQA models without changing the underlying architecture. First, we show that applying RUBi on these architectures leads to important gains over the baselines trained with their original learning strategy. We report a gain of +11.73 accuracy point for SAN and +4.5 for UpDn. This lower gap in accuracy may show that UpDn is less driven by biases than SAN. We report a gain of +3.4 with SAN + RUBi over SAN + Q-Adv + DoE, and +3.06 with UpDn + RUBi over UpDn + Q-Adv + DoE. Full results splitted by question type are available in the supplementary materials.