Exploiting Query Knowledge Embedding and Trilinear Joint Embedding for Visual Question Answering

Zheng Chen, Yaxin Wen

Abstract: Visual Question Answering (VQA) aims to answer natural language questions about a given image. Researchers generally believe that incorporating external knowledge can improve VQA task’s performance. However, existing methods face limitations in acquiring and utilizing such knowledge, preventing them from effectively enhancing a model’s question-answering capability. In this paper, we propose a novel VQA approach based on question-query for Knowledge Embedding. In our approach, we design question query rules to obtain critical external knowledge and then embed this knowledge by integrating it with the question as input features for text modalities. Traditional multimodal feature fusion techniques rely solely on local features, which may result in the loss of global information. To address this issue, we introduce a feature fusion method based on Trilinear Joint Embedding. Utilizing an attention mechanism, we generate a feature matrix composed of question, knowledge, and image components. This matrix is then trilinearly joint embedded to form a novel global feature vector. Due to the computational challenges associated with high-dimensional vectors produced during the trilinear joint embedding process, we employ Tensor Decomposition to break down this vector into a sum of several low-rank tensors. Subsequently, we input the global feature vector into a classifier to obtain the answer in a multi-category classification fashion. Experimental results on the VQAv2, OKVQA, and VizWiz public datasets demonstrate that our approach can achieve accuracy improvements of 1.78%, 3.95%, and 1.16%. Our code are available at https://github.com/yxNoth/KB-VLT.