Counterfactual VQA: A Cause-Effect Look at Language Bias
Table 5: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v1 test split. We reimplement the baselines for fair comparison.
['UpDn', 'All', 'Y/N', 'Num.', 'Other']
Specifically, CF-VQA improves RUBi, Harmonic and Sum in most cases. The exception is using UpDn baseline architecture with Harmonic fusion strategy, where using CF-VQA slightly decreases the accuracy from 55.75% to 55.16%.