@inproceedings{li-etal-2023-multi-modal-debiasing,
    title = "A Multi-modal Debiasing Model with Dynamical Constraint for Robust Visual Question Answering",
    author = "Li, Yu  and
      Hu, Bojie  and
      Zhang, Fengshuo  and
      Yu, Yahan  and
      Liu, Jian  and
      Chen, Yufeng  and
      Xu, Jinan",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.311",
    doi = "10.18653/v1/2023.findings-acl.311",
    pages = "5032--5045",
    abstract = "Recent studies have pointed out that many well-developed Visual Question Answering (VQA) systems suffer from bias problem. Despite the remarkable performance gained on In-Distribution (ID) datasets, the VQA model might merely capture the superficial correlation from question to answer rather than showing real reasoning abilities. Therefore, when switching to Out-of-Distribution (OOD) dataset, whose test distribution is unknown or even reversed with the training set, significant drops might be demonstrated. Although efforts have been devoted to easing the negative bias effect brought by language prior and analysing its inherent cause, they are still limited by the following two aspects. First, most current debiasing methods achieve promising OOD generalization ability with a major sacrifice of the ID performance. Second, existing researches are restricted by exploiting comprehensive biases, since weakening the language bias is mainly focused, while only a few works consider vision bias. In this paper, we investigate a straightforward way to mitigate bias problem for VQA task. Specifically, we reduce bias effect by subtracting bias score from standard VQA base score. Based on such a direct strategy, we design two bias learning branches to detect more bias information, which are combined with a dynamical constraint loss to alleviate the problem of over-correction and insufficient debiasing effect. We evaluate our method on the challenging VQA v2.0 and VQA-CP V2,0 datasets and the proposed method achievessignificant improvement.",
}

Recent studies have pointed out that many well-developed Visual Question Answering (VQA) systems suffer from bias problem. Despite the remarkable performance gained on In-Distribution (ID) datasets, the VQA model might merely capture the superficial correlation from question to answer rather than showing real reasoning abilities. Therefore, when switching to Out-of-Distribution (OOD) dataset, whose test distribution is unknown or even reversed with the training set, significant drops might be demonstrated. Although efforts have been devoted to easing the negative bias effect brought by language prior and analysing its inherent cause, they are still limited by the following two aspects. First, most current debiasing methods achieve promising OOD generalization ability with a major sacrifice of the ID performance. Second, existing researches are restricted by exploiting comprehensive biases, since weakening the language bias is mainly focused, while only a few works consider vision bias. In this paper, we investigate a straightforward way to mitigate bias problem for VQA task. Specifically, we reduce bias effect by subtracting bias score from standard VQA base score. Based on such a direct strategy, we design two bias learning branches to detect more bias information, which are combined with a dynamical constraint loss to alleviate the problem of over-correction and insufficient debiasing effect. We evaluate our method on the challenging VQA v2.0 and VQA-CP V2,0 datasets and the proposed method achievessignificant improvement.
Li, Yu Hu, Bojie Zhang, Fengshuo Yu, Yahan Liu, Jian Chen, Yufeng Xu, Jinan
A Multi-modal Debiasing Model with Dynamical Constraint for Robust Visual Question Answering