215 citations · 215 across the 2 of their papers we have counts for
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cs.CV2018
Dynamic Fusion with Intra- and Inter- Modality Attention Flow for Visual Question Answering
Gao Peng, Zhengkai Jiang, Haoxuan You +4
Learning effective fusion of multi-modality features is at the heart of visual question answering. We propose a novel method of dynamically fusing multi-modal features with intra-…
cs.CV2018
Question-Guided Hybrid Convolution for Visual Question Answering
Peng Gao, Pan Lu, Hongsheng Li +4
In this paper, we propose a novel Question-Guided Hybrid Convolution (QGHC) network for Visual Question Answering (VQA). Most state-of-the-art VQA methods fuse the high-level textu…
cs.CV2018
R-VQA: Learning Visual Relation Facts with Semantic Attention for Visual Question Answering
Pan Lu, Lei Ji, Wei Zhang +3
Recently, Visual Question Answering (VQA) has emerged as one of the most significant tasks in multimodal learning as it requires understanding both visual and textual modalities. E…