Counterfactual VQA: A Cause-Effect Look at Language Bias
Table 7: Improving RUBi cadene2019rubi and Learned-Mixin (LM) clark2019don using our CF-VQA on VQA-CP v2 test split. We reimplement these methods for fair comparison.
['[EMPTY]', 'All', 'Y/N', 'Num.', 'Other']
Learned-Mixin (LM) clark2019don by replacing NIE with TIE for inference. Therefore, CF-VQA could improve RUBi and LM without re-training the model, and the hyper-parameter can be selected on one trained model. As a comparison, LM+H clark2019don needs to re-train LM model with different entropy penalty, which leads to a high computation cost. Therefore, these two fusion strategies are not preferred in practice.