In multi-modal reasoning tasks, such as visual question answering (VQA), there have been many modeling and training paradigms tested. Previous models propose different methods for the vision and language tasks, but which ones perform the best while being sample and computationally efficient? Based on our experiments, we find that representing the text as probabilistic programs and images as object-level scene graphs best satisfy these desiderata. We extend existing models to leverage these soft programs and scene graphs to train on question answer pairs in an end-to-end manner. Empirical results demonstrate that this differentiable end-to-end program executor is able to maintain state-of-the-art accuracy while being sample and computationally efficient.
@article{arxiv.2103.11537,
title = {How to Design Sample and Computationally Efficient VQA Models},
author = {Karan Samel and Zelin Zhao and Binghong Chen and Kuan Wang and Robin Luo and Le Song},
journal= {arXiv preprint arXiv:2103.11537},
year = {2021}
}