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Coarse-to-Fine Reasoning for Visual Question Answering. (arXiv:2110.02526v2 [cs.CV] UPDATED)
April 20, 2022, 1:10 a.m. | Binh X. Nguyen, Tuong Do, Huy Tran, Erman Tjiputra, Quang D. Tran, Anh Nguyen
cs.CV updates on arXiv.org arxiv.org
Bridging the semantic gap between image and question is an important step to
improve the accuracy of the Visual Question Answering (VQA) task. However, most
of the existing VQA methods focus on attention mechanisms or visual relations
for reasoning the answer, while the features at different semantic levels are
not fully utilized. In this paper, we present a new reasoning framework to fill
the gap between visual features and semantic clues in the VQA task. Our method
first extracts the …
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