Bi-direction Co-Attention Network on Visual Question Answering for Blind People

The visual impairment community especially blind people needs support from advanced technologies to help them with understanding and answering the image content. In the multi-modal area, Visual Question Answering (VQA) is the notable cutting-edge task requiring the combination of images and texts via a co-attention mechanism. Inspired by the Deep Co-attention Layer, we propose a Bi-direction Co-Attention VT-Transformer network to jointly learn visual and textual features simultaneously. Via our system, the relationship and interaction of the modality objects are digested and combined together into the meaningful space. Besides, the consistency of Transformer architecture in both feature extractor and multi-modal attention function is efficient enough to decrease the layer of attention as well as the computation cost. Through the experimental results and ablation studies, our model achieves the promising performance against the existing approaches and uni-direction mechanism in VizWiz-VQA 2020 dataset for blind people.