Knowledge-aware image understanding with multi-level visual representation enhancement for visual question answering
Author:
Funder
National Natural Science Foundation of China
Key Project of Science and Technology Innovation 2030
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s10994-023-06426-6.pdf
Reference41 articles.
1. Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S., & Zhang, L. (2018). 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).
2. Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D. (2015). VQA: Visual question answering. In Proceedings of the IEEE international conference on computer vision (pp. 2425–2433).
3. Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017). Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 5, 135–146.
4. Borisyuk, F., Gordo, A., & Sivakumar, V. (2018). Rosetta: Large scale system for text detection and recognition in images. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 71–79).
5. Cadene, R., Ben-Younes, H., Cord, M., & Thome, N. (2019). Murel: Multimodal relational reasoning for visual question answering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 1989–1998).
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