Counterfactual VQA: A Cause-Effect Look at Language Bias
Table 1: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test set. We reimplement the baselines for fair comparison.
['UpDn', 'All', 'Y/N', 'Num.', 'Other']
How general is CF-VQA? Here, “RUBi/Harmonic/Sum” means using the language prior based strategies cadene2019rubi; clark2019don that train the ensemble model and test with only the vision-language branch. It can be clearly seen that CF-VQA improves the language prior based strategies by over 2% in all cases. In particular, RUBi and Sum are improved by over 6% with different baseline architectures.