Ablation results.

Existing visual question answering methods typically concentrate only on visual targets in images, ignoring the key textual content in the images, thereby limiting the depth and accuracy of image content comprehension. Inspired by this, we pay attention to the task of text-based visual question answering, address the performance bottleneck issue caused by over-fitting risk in existing self-attention-based models, and propose a scenario text visual question answering method called INT2-VQA that fuses knowledge manifestation based on inter-modality and intra-modality collaborations. Specifically, we model the complementary priori knowledge of locational collaboration between visual targets and textual targets across modalities and the contextual semantical collaboration among textual word targets within a modality. Based on this, a universal knowledge-reinforced attention module is designed to achieve a unified encoding manifestation of both relations. Extensive ablation experiments, contrast experiments, and visual analyses demonstrate the effectiveness of the proposed method and prove its superiority over the other state-of-the-art methods.