Abstract
Visual Question Answering (VQA) tasks must provide correct answers to the questions posed by given images. Such requirement has been a wide concern since this task was presented. VQA consists of four steps: image feature extraction, question text feature extraction, multi-modal feature fusion and answer reasoning. During multi-modal feature fusion, outer product calculation is used in existing models, which leads to excessive model parameters, high training overhead, and slow convergence. To avoid these problems, we applied the Variational Autoencoder (VAE) method to calculate the probability distribution of the hidden variables of image and question text. Furthermore, we designed a question feature hierarchy method based on the traditional attention mechanism model and VAE. The objective is to investigate deep questions and image correlation features to improve the accuracy of VQA tasks.
Student Paper. This work is supported by the Natural Science Foundation of Fujian Province of China (2017J01754). This work is supported by the Natural Science Foundation of Fujian Province of China (2018J01799).
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References
Agrawal, A., et al.: AQA: visual question answering. Int. J. Comput. Vis. 123, 4–31 (2017)
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Elman, J.L.: Finding structure in time. Cogn. Sci. 14, 179–211 (1990)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Donahue, J., et al.: Long-term recurrent convolutional networks for visual recognition and description. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2625–2634 (2015)
Fukui, A., Park, D.H., Yang, D., Rohrbach, A., Darrell, T., Rohrbach, M.: Multimodal compact bilinear pooling for visual question answering and visual grounding. arXiv preprint arXiv:1606.01847 (2016)
Yu, Z., Yu, J., Xiang, C., Fan, J., Tao, D.: Beyond bilinear: generalized multimodal factorized high-order pooling for visual question answering. IEEE Trans. Neural Netw. Learn. Syst. 14, 1–13 (2018)
Xu, H., Saenko, K.: Ask, attend and answer: exploring question-guided spatial attention for visual question answering. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 451–466. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_28
Yang, Z., He, X., Gao, J., Deng, L., Smola, A.: Stacked attention networks for image question answering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 21–29 (2016)
Anderson, P., et al.: Bottom-up and top-down attention for image captioning and visual question answering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6077–6086 (2018)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Zhou, B., Tian, Y., Sukhbaatar, S., Szlam, A., Fergus, R.: Simple baseline for visual question answering. arXiv preprint arXiv:1512.02167 (2015)
Noh, H., Hongsuck Seo, P., Han, B.: Image question answering using convolutional neural network with dynamic parameter prediction. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 30–38 (2016)
Malinowski, M., Rohrbach, M., Fritz, M.: Ask your neurons: a neural-based approach to answering questions about images. In: Proceedings of the IEEE international conference on computer vision, pp. 1–9 (2015)
Wu, Q., Wang, P., Shen, C., Dick, A., van den Hengel, A.: Ask me anything: free-form visual question answering based on knowledge from external sources. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4622–4630 (2016)
Andreas, J., Rohrbach, M., Darrell, T., Klein, D.: Neural module networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 39–48 (2015)
Lu, J., Yang, J., Batra, D., Parikh, D.: Hierarchical question-image co-attention for visual question answering. In: Advances In Neural Information Processing Systems, pp. 289–297 (2016)
Noh, H., Han, B.: Training recurrent answering units with joint loss minimization for VQA. arXiv preprint arXiv:1606.03647 (2016)
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Chen, L., Zhuo, Y., Wu, Y., Wang, Y., Zheng, X. (2019). Multi-modal Feature Fusion Based on Variational Autoencoder for Visual Question Answering. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11858. Springer, Cham. https://doi.org/10.1007/978-3-030-31723-2_56
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