Relational reasoning and adaptive fusion for visual question answering

Visual relationship modeling plays an indispensable role in visual question answering (VQA). VQA models need to fully understand the visual scene and positional relationships within the image to answer complex reasoning questions involving visual object relationships. Accurate reasoning and an understanding of the relationships between different visual objects are particularly crucial. However, most reasoning models used in current VQA tasks only use simple attention mechanisms to model visual object relationships and ignore the potential for effective modeling using rich visual object features during the learning process. This work proposes an effective visual object Relationship Reasoning and Adaptive Fusion (RRAF) model to address the shortcomings of existing VQA model research. RRAF can simultaneously model visual objects’ position, appearance, and semantic features and uses an adaptive fusion mechanism to achieve fine-grained multimodal reasoning and fusion. Specifically, we designed an effective image encoder to model and learn the relationship between the position and appearance features of visual objects. In addition, in the co-attention module, we employ semantic information from the question to focus on critical visual objects. Finally, we use an adaptive fusion mechanism to reassign weights and fuse different modalities of features to effectively predict the answer. Experimental results show that the RRAF model outperforms current state-of-the-art methods on the VQA 2.0 and GQA datasets, especially in visual object counting problems. We also conducted extensive ablation experiments to demonstrate the effectiveness of the RRAF model, achieving an overall accuracy of 71.33 https://github.com/shenxiang-vqa/RRAF .