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On the Significance of Question Encoder Sequence Model in the Out-of-Distribution Performance in Visual Question Answering
This paper shows that the sequence model architecture used in the question-encoder has a significant role in the generalizability of VQA models.
Linguistically-aware Attention for Reducing the Semantic-Gap in Vision-Language Tasks
Humans perform such a correlation with a strong linguistic understanding of the visual world.
Reducing Language Biases in Visual Question Answering with Visually-Grounded Question Encoder
We demonstrate the effect of VGQE on three recent VQA models and achieve state-of-the-art results on the bias-sensitive split of the VQAv2 dataset; VQA-CPv2.
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