Collaborative reasoning for knowledge-based visual question answering is challenging but vital and efficient in understanding the features of the images and questions. While previous methods jointly fuse all kinds of features by attention mechanism or use handcrafted rules to generate a layout for performing compositional reasoning, which lacks the process of visual reasoning and introduces a large number of parameters for predicting the correct answer. For conducting visual reasoning on all kinds of image–question pairs, in this paper, we propose a novel reasoning model of a question-guided tree structure with a knowledge base (QGTSKB) for addressing these problems. In addition, our model consists of four neural module networks: the attention model that locates attended regions based on the image features and question embeddings by attention mechanism, the gated reasoning model that forgets and updates the fused features, the fusion reasoning model that mines high-level semantics of the attended visual features and knowledge base and knowledge-based fact model that makes up for the lack of visual and textual information with external knowledge. Therefore, our model performs visual analysis and reasoning based on tree structures, knowledge base and four neural module networks. Experimental results show that our model achieves superior performance over existing methods on the VQA v2.0 and CLVER dataset, and visual reasoning experiments prove the interpretability of the model.

In this paper, we provide external image features and use the internal attention mechanism to solve the VQA problem given a dataset of textual questions and related images. Most previous models for VQA use a pair of images and questions as input. In addition, the model adopts a question-oriented attention mechanism to extract the features of the entire image and then perform feature fusion. However, the shortcoming of these models is that they cannot effectively eliminate the irrelevant features of the image. In addition, the problem-oriented attention mechanism lacks in the mining of image features, which will bring in redundant image features. In this paper, we propose a VQA model based on adversarial learning and bidirectional attention. We exploit external image features that are not related to the question to form an adversarial mechanism to boost the accuracy of the model. Target detection is performed on the image—that is, the image-oriented attention mechanism. The bidirectional attention mechanism is conducive to promoting model attention and eliminating interference. Experimental results are evaluated on benchmark datasets, and our model performs better than other models based on attention methods. In addition, the qualitative results show the attention maps on the images and leads to predicting correct answers.