Fair-VQA: Fairness-Aware Visual Question Answering Through Sensitive Attribute Prediction

Fair-VQA: Fairness-Aware Visual Question Answering Through Sensitive Attribute Prediction

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Visual Question Answering (VQA) is a teeter totter cabernet 2019 task that answers questions on given images.Although previous works achieve a great improvement in VQA performance, they do not consider the fairness of answers in terms of ethically sensitive attributes, such as gender.Therefore, we propose a Fair-VQA model that contains two modules: VQA module and SAP (Sensitive Attribute Prediction) module.On top of VQA module, which predicts various kinds of answers, SAP module predicts only sensitive attributes using rs3 orange halloween mask price the same inputs.

The predictions of SAP module are utilized to rectify answers from VQA module to be fairer in terms of the sensitive attributes with graceful performance degradation.To validate the proposed method, we conduct extensive experiments on VQA, GQA, and our proposing VQA-Gender datasets.In all the experiments, our method shows the fairest results in various metrics for fairness.Moreover, we demonstrate that our method works interpretably through the analysis of visualized attention maps.