MUREL: Multimodal Relational Reasoning for Visual Question Answering
Table 3: State-of-the-art comparison on the VQA 2.0 dataset. Results on test-dev and test-std splits. All these models were trained on the same training set (VQA 2.0 train+val), using the Bottom-up features provided by [3]. No ensembling methods have been used. † have been trained by [6].
['Model', '[ITALIC] test-dev Yes/No', '[ITALIC] test-dev Num.', '[ITALIC] test-dev Other', '[ITALIC] test-dev All', '[ITALIC] test-std All']
S4SS3SSS0Px1 Vqa 2.0 This tends to indicate that VQA models can benefit from retaining local information in mulitmodal vectors instead of scalar coefficients. This shows the interest of our spatial-semantic pairwise modeling between all possible pairs of regions. Finally, even though we did not extensively tune the hyperparameters of our model, our overall score on the test-dev split is highly competitive with state-of-the-art methods. Please note that they improve their overall scores up to 70.01% when they include multiple types of visual features and more training data.