Misogynous Memes Recognition: Training vs Inference Bias Mitigation Strategies
In this paper, we address the problem of automatic misogynous meme recognition by dealing with potentially biased elements that could lead to unfair models. In particular, a bias estimation technique is used to identify those textual and visual elements that unintendedly affect the model prediction,...
Gorde:
| Argitaratua izan da: | URI:https://journals.openedition.org/ijcol, |
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| Egile Nagusiak: | , , |
| Formatua: | Article ou chapitre numérique |
| Hizkuntza: | Anglais |
| Argitaratua: |
IJCoL
2025
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| Sarrera elektronikoa: | Accès Université d'Orléans et IFPM |
| Gaia: | In this paper, we address the problem of automatic misogynous meme recognition by dealing with potentially biased elements that could lead to unfair models. In particular, a bias estimation technique is used to identify those textual and visual elements that unintendedly affect the model prediction, and a few bias mitigation methods are proposed, investigating two different types of debiasing strategies, i.e., at training time and at inference time. The proposed approaches achieve remarkable results both in terms of prediction and generalization capabilities. |
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