Towards VQA Models That Can Read
Table 1: Single model VQA 2.0 and VizWiz performance in %. Our revised implementation of Pythia, v0.3, with LoRRA outperforms or is comparable to state-of-the-art on VQA 2.0.
['[BOLD] VQA 2.0 Accuracy  [BOLD] Model', '[BOLD] VQA 2.0 Accuracy  [BOLD] test-dev']
v2.0 dataset Ablations. We create several ablations of our approach LoRRA by using the reading component and answering module in conjunction and alternatively. (i) I+Q: Pythia+O : Pythia with OCR features as input but no copy module or dynamic answer space; (iii) Pythia+O+C: (ii) with the copy mechanism but no fixed answer space i.e. the model can only predict from the OCR tokens. Abbreviation C is used when we add the copy module and dynamic answer space to a model. Interestingly, when adding LoRRA to Pythia it improves accuracy from 68.71 to 69.21 on VQA 2.0 (see Tab.