ACL’2020 Cross-Modality Relevance for Reasoning on Language and Vision
Table 2: Accuracy on VQA v2.0.
['Model', 'Dev% Overall', 'Test Standard% Y/N', 'Test Standard% Num', 'Test Standard% Other', 'Test Standard% Overall']
VQA v2.0: Most competitive models are based on Transformers (ViLBERT Lu et al. BUTD Anderson et al. The proposed CMR achieves the best test accuracy on Y/N questions and Other questions. However, CMR does not achieve the best performance on Number questions. This is because Number questions require the ability to count numbers in one modality while CMR focuses on modeling relations between modalities. Performance on counting might be improved by explicit modeling of quantity representations. CMR also achieves the best overall accuracy. In particular, we can see a 2.3% improvement over VisualBERT Li et al. This shows the significance of the entity and relational relevance.