RUBi: Reducing Unimodal Biases for Visual Question Answering
Table 1: State-of-the-art results on VQA-CP v2 test. All reported models use the same features from Anderson_2018_CVPR . Models with * have been trained by ramakrishnan2018overcoming . Models with ** have been trained by shrestha2019answer .
['Model', 'Overall', 'Answer type Yes/No', 'Answer type Number', 'Answer type Other']
, we compare our approach consisting of our baseline architecture trained with RUBi on VQA-CP v2 against the state of the art. We compute the average accuracy over 5 experiments with different random seeds. Our RUBi approach reaches an average overall accuracy of 47.11% with a low standard deviation of ±0.51. RUBi reaches a +8.65 improvement over our baseline model trained with the classical cross-entropy. In comparison, the second best approach UpDn + Q-Adv + DoE only achieves a +1.43 gain in overall accuracy over their baseline UpDn. In addition, our approach does not significantly reduce the accuracy over our baseline for the answer type Other, while the second best approach reduces it by 10.57 point.