Inferential Visual Question Generation-Reference-Cited by-同舟云学术

Inferential Visual Question Generation

Published:2022-10-10 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 30th ACM International Conference on Multimedia
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Author:

Bi Chao¹,Wang Shuhui²,Xue Zhe³,Chen Shengbo⁴,Huang Qingming¹

Affiliation:

1. University of the Chinese Academy of Sciences; Institute of Computing Technology, Chinese Academy of Sciences, Beijing; Beijing, China

2. Institute of Computing Technology, Chinese Academy of Sciences; Peng Cheng Laboratory, Beijing; Shenzhen, China

3. Beijing University of Posts and Telecommunications, Beijing, China

4. Henan University, Kaifeng, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3503161.3548055

Reference53 articles.

1. Analyzing the Behavior of Visual Question Answering Models

2. Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering

3. Neural Module Networks

4. VQA: Visual Question Answering

5. Satanjeev Banerjee and Alon Lavie . 2005 . METEOR: An automatic metric for MT evaluation with improved correlation with human judgments . In Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization. 65--72 . Satanjeev Banerjee and Alon Lavie. 2005. METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. In Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization. 65--72.

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