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Article overview
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Generative Bias for Visual Question Answering | | Jae Won Cho
; Dong-jin Kim
; Hyeonggon Ryu
; In So Kweon
; | | Date: |
1 Aug 2022 | | Abstract: | The task of Visual Question Answering (VQA) is known to be plagued by the
issue of VQA models exploiting biases within the dataset to make its final
prediction. Many previous ensemble based debiasing methods have been proposed
where an additional model is purposefully trained to be biased in order to aid
in training a robust target model. However, these methods compute the bias for
a model from the label statistics of the training data or directly from single
modal branches. In contrast, in this work, in order to better learn the bias a
target VQA model suffers from, we propose a generative method to train the bias
model emph{directly from the target model}, called GenB. In particular, GenB
employs a generative network to learn the bias through a combination of the
adversarial objective and knowledge distillation. We then debias our target
model with GenB as a bias model, and show through extensive experiments the
effects of our method on various VQA bias datasets including VQA-CP2, VQA-CP1,
GQA-OOD, and VQA-CE. | | Source: | arXiv, 2208.00690 | | Services: | Forum | Review | PDF | Favorites | |
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