Dataset Bias Mitigation in Multiple-Choice Visual Question Answering and Beyond

• URL: http://arxiv.org/abs/2310.14670v2
• Date: Tue, 31 Oct 2023 20:49:11 GMT
• Title: Dataset Bias Mitigation in Multiple-Choice Visual Question Answering and Beyond
• Authors: Zhecan Wang, Long Chen, Haoxuan You, Keyang Xu, Yicheng He, Wenhao Li, Noel Codella, Kai-Wei Chang, Shih-Fu Chang
• Abstract summary: Vision-language (VL) understanding tasks evaluate models' comprehension of complex visual scenes through multiple-choice questions. We have identified two dataset biases that models can exploit as shortcuts to resolve various VL tasks correctly without proper understanding. We propose Adversarial Data Synthesis (ADS) to generate synthetic training and debiased evaluation data. We then introduce Intra-sample Counterfactual Training (ICT) to assist models in utilizing the synthesized training data, particularly the counterfactual data, via focusing on intra-sample differentiation.
• Score: 93.96982273042296
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-language (VL) understanding tasks evaluate models' comprehension of complex visual scenes through multiple-choice questions. However, we have identified two dataset biases that models can exploit as shortcuts to resolve various VL tasks correctly without proper understanding. The first type of dataset bias is \emph{Unbalanced Matching} bias, where the correct answer overlaps the question and image more than the incorrect answers. The second type of dataset bias is \emph{Distractor Similarity} bias, where incorrect answers are overly dissimilar to the correct answer but significantly similar to other incorrect answers within the same sample. To address these dataset biases, we first propose Adversarial Data Synthesis (ADS) to generate synthetic training and debiased evaluation data. We then introduce Intra-sample Counterfactual Training (ICT) to assist models in utilizing the synthesized training data, particularly the counterfactual data, via focusing on intra-sample differentiation. Extensive experiments demonstrate the effectiveness of ADS and ICT in consistently improving model performance across different benchmarks, even in domain-shifted scenarios.

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