Learning neighbor-enhanced region representations and question-guided visual representations for visual question answering-Reference-Cited by-同舟云学术

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Learning neighbor-enhanced region representations and question-guided visual representations for visual question answering

Published:2024-03 Issue: Volume:238 Page:122239
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Gao Ling^ORCID,Zhang Hongda,Sheng Nan^ORCID,Shi Lida,Xu Hao^ORCID

Funder

National Natural Science Foundation of China

Department of Science and Technology of Jilin Province

Jilin Scientific and Technological Development Program

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference72 articles.

1. Anderson, P., He, X., Buehler, C., et al. (2018). Bottom-Up and Top-Down Attention for Image Captioning and VQA. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 6077–6086).

2. Andreas, J., Rohrbach, M., Darrell, T., et al. (2016a). Learning to Compose Neural Networks for Question Answering. In Proceedings of NAACL-HLT (pp. 1545–1554).

3. Andreas, J., Rohrbach, M., Darrell, T., et al. (2016b). Neural module networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 39–48).

4. Antol, S., Agrawal, A., Lu, J., et al. (2015). Vqa: Visual question answering. In Proceedings of the IEEE international conference on computer vision (pp. 2425–2433).

5. Ben-Younes, H., Cadene, R., Cord, M., et al. (2017). MUTAN: Multimodal Tucker Fusion for Visual Question Answering. In Proceedings of the IEEE international conference on computer vision (pp. 2612–2620).

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. PGCL: Prompt guidance and self-supervised contrastive learning-based method for Visual Question Answering;Expert Systems with Applications;2024-10

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