Recently, a large amount of attention-based deeplearning methods have been proposed for VQA, including top-down (Ren et al., 2015a;Fukui et al., 2016;Wu et al., 2016;Goyal et al., 2017;Li et al., 2018a) and bottom-up attention methods (Anderson et al., 2018;Li et al., 2018b;Wu and Mooney, 2019).
a typical model first extracts image features using a pre-trained CNN, and then trains an RNN to encode the question, using an attention mechanism to focus on specific features of the image.