Web: http://arxiv.org/abs/2207.08739

Sept. 16, 2022, 1:16 a.m. | Long Chen, Yuhang Zheng, Jun Xiao

cs.CL updates on arXiv.org arxiv.org

Data Augmentation (DA) -- generating extra training samples beyond original
training set -- has been widely-used in today's unbiased VQA models to mitigate
the language biases. Current mainstream DA strategies are synthetic-based
methods, which synthesize new samples by either editing some visual
regions/words, or re-generating them from scratch. However, these synthetic
samples are always unnatural and error-prone. To avoid this issue, a recent DA
work composes new augmented samples by randomly pairing pristine images and
other human-written questions. Unfortunately, to …

arxiv augmentation data question answering

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