Improving VQA and its Explanations by Comparing Competing Explanations (2021)
Most recent state-of-the-art Visual Question Answering (VQA) systems are opaque black boxes that are only trained to fit the answer distribution given the question and visual content. As a result, these systems frequently take shortcuts, focusing on simple visual concepts or question priors. This phenomenon becomes more problematic as the questions become complex that requires more reasoning and commonsense knowledge. To address this issue, we present a novel framework that uses explanations for competing answers to help VQA systems select the correct answer. By training on human textual explanations, our framework builds better representations for the questions and visual content, and then reweights confidences in the answer candidates using either generated or retrieved explanations from the training set. We evaluate our framework on the VQA-X dataset, which has more difficult questions with human explanations, achieving new state-of-the-art results on both VQA and its explanations.
PDF, Arxiv
In The AAAI Conference on Artificial Intelligence (AAAI), Explainable Agency in Artificial Intelligence Workshop, Vol. arXiv:2006.15631, February 2021.

Slides (PDF)
Liyan Chen Formerly affiliated Ph.D. Student liyanc [at] cs utexas edu
Raymond J. Mooney Faculty mooney [at] cs utexas edu
Jialin Wu Ph.D. Student jialinwu [at] utexas edu