VizWiz Grand Challenge: Answering Visual Questions from Blind People
Table 3: Performance of VQA methods on the VizWiz test data with respect to four metrics. Results are shown for three variants of three methods [6, 18, 24]: use models as is, fine-tuned (FT), and trained on only VizWiz data (VizWiz). The methods use different combinations of image (I), question (Q), and attention (A) models.
['Method', 'Acc', 'CIDEr', 'BLEU', 'METEOR']
We find little performance difference between fine-tuning and training from scratch for the three models. While the number of training examples in VizWiz is relatively small, we hypothesize the size is sufficient for teaching the models to retain knowledge about answer categories that are applicable in this setting. Despite the improvements, further work is still needed to achieve human performance (i.e., 0.75 accuracy)