MAVias: Mitigate any Visual Bias

Ioannis Sarridis, Christos Koutlis, Symeon Papadopoulos, Christos Diou; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025, pp. 1271-1281

Mitigating biases in computer vision models is an essential step towards trustworthy artificial intelligence systems. Existing bias mitigation methods are limited to predefined biases, preventing their use in visual datasets where multiple, possibly unknown biases exist. To address this limitation, we introduce MAVias, an open-set bias mitigation approach that leverages foundation models to discover spurious associations between visual attributes and target classes. MAVias first captures a wide variety of visual features in natural language via a foundation image tagging model, and then leverages a large language model to select visual features that define the target class, resulting in a set of language-coded potential visual biases. It then translates these biases into vision-language embeddings and introduces an in-processing bias mitigation approach to prevent the model from encoding information related to them. Experiments on diverse datasets, including CelebA, Waterbirds, ImageNet, and UrbanCars, show that MAVias effectively detects and mitigates a wide range of biases in visual recognition tasks, outperforming current state-of-the-art.