Towards Causal VQA: Revealing and Reducing Spurious Correlations by Invariant and Covariant Semantic Editing
Table 3: Accuracy (in %) of different models when trained on VQA v2 train and tested on VQA v2 val.
['Trained by us CNN+LSTM', 'Trained by us 53.32', 'For comparison d-LSTM Q + norm I ', 'For comparison 51.61']
VQA models and training. We modified this SAAA code in order to get CL model by removing the attention layers from the network. As we use the VQA v2 val split for consistency evaluation and testing, the models are trained using only the train split. Further details of these models and hyper-parameters used can be found in the supplementary (section B.1). The models chosen by us exceed the performance of other models within the respective categories.