Unveiling Cross Modality Bias in Visual Question Answering: A Causal View with Possible Worlds VQA

TL;DR

This paper introduces a causal inference approach to simultaneously reduce vision and language biases in Visual Question Answering (VQA), improving generalization and accuracy by addressing confounding effects.

Contribution

It models the confounding effects of vision and language biases in VQA and proposes a novel counterfactual inference method to mitigate these biases concurrently.

Findings

Outperforms state-of-the-art on VQA-CP v2 dataset

Effectively reduces both vision and language biases

Improves question-answering accuracy with numerical answers

Abstract

To increase the generalization capability of VQA systems, many recent studies have tried to de-bias spurious language or vision associations that shortcut the question or image to the answer. Despite these efforts, the literature fails to address the confounding effect of vision and language simultaneously. As a result, when they reduce bias learned from one modality, they usually increase bias from the other. In this paper, we first model a confounding effect that causes language and vision bias simultaneously, then propose a counterfactual inference to remove the influence of this effect. The model trained in this strategy can concurrently and efficiently reduce vision and language bias. To the best of our knowledge, this is the first work to reduce biases resulting from confounding effects of vision and language in VQA, leveraging causal explain-away relations. We accompany our method with an explain-away strategy, pushing the accuracy of the questions with numerical answers results compared to existing methods that have been an open problem. The proposed method outperforms the state-of-the-art methods in VQA-CP v2 datasets.