  We tackle the challenge of Visual Question Answering in multi-image setting
for the ISVQA dataset. Traditional VQA tasks have focused on a single-image
setting where the target answer is generated from a single image. Image set
VQA, however, comprises of a set of images and requires finding connection
between images, relate the objects across images based on these connections and
generate a unified answer. In this report, we work with 4 approaches in a bid
to improve the performance on the task. We analyse and compare our results with
three baseline models - LXMERT, HME-VideoQA and VisualBERT - and show that our
approaches can provide a slight improvement over the baselines. In specific, we
try to improve on the spatial awareness of the model and help the model
identify color using enhanced pre-training, reduce language dependence using
adversarial regularization, and improve counting using regression loss and
graph based deduplication. We further delve into an in-depth analysis on the
language bias in the ISVQA dataset and show how models trained on ISVQA
implicitly learn to associate language more strongly with the final answer.
