Dataset bias is a significant problem in training fair classifiers. When
attributes unrelated to classification exhibit strong biases towards certain
classes, classifiers trained on such dataset may overfit to these bias
attributes, substantially reducing the accuracy for minority groups. Mitigation
techniques can be categorized according to the availability of bias information
(\ie, prior knowledge). Although scenarios with unknown biases are better
suited for real-world settings, previous work in this field often suffers from
a lack of interpretability regarding biases and lower performance. In this
study, we propose a framework to identify potential biases as keywords without
prior knowledge based on the partial occurrence in the captions. We further
propose two debiasing methods: (a) handing over to an existing debiasing
approach which requires prior knowledge by assigning pseudo-labels, and (b)
employing data augmentation via text-to-image generative models, using acquired
bias keywords as prompts. Despite its simplicity, experimental results show
that our framework not only outperforms existing methods without prior
knowledge, but also is even comparable with a method that assumes prior
knowledge.

在这项研究中，我们提出了一种在没有先验知识的情况下通过关键词的部分出现来识别潜在偏见的框架，并进一步提出了两种去偏方法：(a) 通过指定伪标签将其传递给现有的需要先验知识的去偏方法，以及 (b) 通过文本到图像生成模型进行数据增强，使用获得的偏见关键词作为提示。实验结果表明，尽管简单，我们的框架不仅能在没有先验知识的情况下胜过现有方法，而且甚至可以与假设有先验知识的方法媲美。