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Visual Question Answering (VQA) has achieved great success thanks to the fast development of deep neural networks (DNN).
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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)
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Hu, R., Andreas, J., Rohrbach, M., Darrell, T., Saenko, K.: Learning to reason: End-to-end module networks for visual question answering. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 804–813 (2017)
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Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.J., Shamma, D.A., et al.: Visual genome: Connecting language and vision using crowdsourced dense image annotations. International journal of computer vision 123
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Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S., Zhang, L.: 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)
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Teney, D., Anderson, P., He, X., Van Den Hengel, A.: Tips and tricks for visual question answering: Learnings from the 2017 challenge. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4223–4232 (2018)
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Shah, M., Chen, X., Rohrbach, M., Parikh, D.: Cycle-consistency for robust visual question answering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 6649–6658 (2019)
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