QSFVQA: A Time Efficient, Scalable and Optimized VQA Framework
Author:
Publisher
Springer Science and Business Media LLC
Subject
Multidisciplinary
Link
https://link.springer.com/content/pdf/10.1007/s13369-023-07661-8.pdf
Reference50 articles.
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3. Yang, C.; Jiang, M.; Jiang, B.; Zhou, W.; Li, K.: Co-attention network with question type for visual question answering. IEEE Access 7, 40771–40781 (2019)
4. Zhang, L.; Liu, S.; Liu, D.; Zeng, P.; Li, X.; Song, J.; Gao, L.: Rich visual knowledge-based augmentation network for visual question answering. IEEE Trans. Neural Netw. Learn. Syst. 32(10), 4362–4373 (2020)
5. 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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