Abstract: To better explore potential common sense information in images,this paper introduces an innovative Visual common sense feature for the visual question answering(VQA) task,and effectively integrate the bottom-up feature with the visual common sense feature through the visual feature fusion module.Thus,rich visual feature representation is realized.Guided attention fusion method,through the input of bottom-up features and visual common sense features into the information interaction mo-dule,enables the attention mechanism to capture the image content more relevant to the problem text.On this basis,this paper also designs and introduces a gated counting module(GCM) to retain the number of entities in image features.This module significantly improves model performance on counting problems while maintaining information integrity and relevance.Compared to traditional methods,GCM is able to handle visual problems involving quantities more accurately,thus enhancing the accuracy of the overall VQA task.Finally,we have carried out a large number of experiments on the widely used dataset VQA v2.0 and obtained relatively good results.

Key words: Visual question answering, Visual common sense feature, Feature fusion, Visual feature, Faster R-CNN, Gate counting module