Research on visual question answering system based on deep neural network model

Xushuo Tang

doi:10.1117/12.2669805

28 March 2023 Research on visual question answering system based on deep neural network model

Xushuo Tang

Proceedings Volume 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022); 1256651 (2023) https://doi.org/10.1117/12.2669805
Event: Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 2022, Chongqing, China

Abstract

With the rapid development of deep learning, computer vision, natural semantic processing and other technologies, Visual Question Answering (VQA) has gradually become to be an important research direction in the multimodal field. VQA technology has a wide range of application scenarios, such as multi-modal search, medical consultation, intelligent driving and so on, and has become the research hotspot and focus of scholars. Based on the classical CLEVR dataset, this paper builds different VQA prediction models by using the BilSTM model and different deep learning algorithms, including modnet, VGG and ResNet models. The results show that the hybrid model of ResNet and BiLSTM has the highest prediction accuracy, reaching 0.978. Compared with other algorithms, the accuracy is 1.3%, 2.5% and 2.7% higher than that of MobileMet, VGG19 and VGG16 models, which means that the hybrid model of resnet50 and BiLSTM is more effective in VQA tasks. At the same time, some problems and deficiencies of our model are analyzed and the future improvement direction is given.

Citation Download Citation

Xushuo Tang "Research on visual question answering system based on deep neural network model", Proc. SPIE 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 1256651 (28 March 2023); https://doi.org/10.1117/12.2669805

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KEYWORDS

Evolutionary algorithms

Data modeling

Visual process modeling

Neural networks

Performance modeling

Deep learning

Semantics

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