MAVias: Mitigate any Visual Bias
Ioannis Sarridis, Christos Koutlis, Symeon Papadopoulos, and Christos Diou

TL;DR
MAVias is a novel open-set bias mitigation method that uses foundation models to discover and mitigate diverse visual biases in datasets, improving fairness in computer vision models.
Contribution
The paper introduces MAVias, a new approach leveraging foundation models for discovering and mitigating multiple, unknown visual biases in datasets.
Findings
Effectively detects and mitigates a wide range of biases.
Outperforms current state-of-the-art bias mitigation methods.
Works across diverse datasets like CelebA, Waterbirds, ImageNet, UrbanCars.
Abstract
Mitigating biases in computer vision models is an essential step towards the trustworthiness of artificial intelligence models. Existing bias mitigation methods focus on a small set of predefined biases, limiting their applicability in visual datasets where multiple, possibly unknown biases exist. To address this limitation, we introduce MAVias, an open-set bias mitigation approach leveraging 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 those visual features defining the target class, resulting in a set of language-coded potential visual biases. We then translate this set of potential biases into vision-language embeddings and introduce an in-processing bias mitigation…
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Taxonomy
TopicsErgonomics and Musculoskeletal Disorders
MethodsSparse Evolutionary Training · Focus
