Visual question answering (VQA) is regarded as a multi-modal fine-grained feature fusion task, which requires the construction of multi-level and omnidirectional relations between nodes. One main solution is the composite attention model which is composed of co-attention (CA) and self-attention(SA). However, the existing composite models only consider the stack of single attention blocks, lack of path-wise historical memory, and overall adjustments. We propose a path attention memory network (PAM) to construct a more robust composite attention model. After each single-hop attention block (SA or CA), the importance of the cumulative nodes is used to calibrate the signal strength of nodes’ features. Four memoried single-hop attention matrices are used to obtain the path-wise co-attention matrix of path-wise attention (PA); therefore, the PA block is capable of synthesizing and strengthening the learning effect on the whole path. Moreover, we use guard gates of the target modal to check the source modal values in CA and conditioning gates of another modal to guide the query and key of the current modal in SA. The proposed PAM is beneficial to construct a robust multi-hop neighborhood relationship between visual and language and achieves excellent performance on both VQA2.0 and VQA-CP V2 datasets.

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