Abstract
Visual Question Answering (VQA) has received increasing attention in NLP research. Most VQA images focus on natural scenes. However, some images widely used in textbooks such as diagrams often contain complicated and abstract information (e.g. constructed graphs with logic and concepts). Therefore, Diagram Question answering (DQA) is a challenging but significant task, which is also helpful for machines to understand human cognitive behaviors and learning habits. On DQA task, we propose a multi-perspective understanding based visual question-answering method, which constructs a variety of different self-monitoring tasks in the form of prompts to help the model learn deeper information. For the first time, we propose a decoding method of “Cross Entropy constraint Decoding”, which can effectively constrain the content generated by the text when performing multiple selection tasks. This method has obtained SOTA in the evaluation task of CCKS-2022, which fully proves the effectiveness of the method.
M. Zhu and Y. Weng—Contributed equally to this work.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Antol, S., et al.: Visual question answering. In: International Conference on Computer Vision, VQA (2015)
Chen, S.X., Liu, J.S.: Statistical applications of the poisson-binomial and conditional Bernoulli distributions. Statistica Sinica 7, 875–892 (1997)
Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long and Short Papers) (Minneapolis, Minnesota, June 2019), Association for Computational Linguistics, pp. 4171–4186
Goyal, Y., Khot, T., Agrawal, A., Summers-Stay, D., Batra, D., Parikh, D.: Making the V in VQA matter: elevating the role of image understanding in visual question answering. Int. J. Comput. Vis. 127(4), 398–414 (2018). https://doi.org/10.1007/s11263-018-1116-0
Han, X., et al.: Pre-trained models: past, present and future. AI Open 2, 225–250 (2021)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv:1512.03385 Computer Vision and Pattern Recognition (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Hu, R., Singh, A.: Unit: multimodal multitask learning with a unified transformer. In: International Conference on Computer Vision (2021)
Huang, Y., Lv, T., Cui, L., Lu, Y., Wei, F.: Layoutlmv3: pre-training for document AI with unified text and image masking
Li, B., Weng, Y., Sun, B., Li, S.: Towards visual-prompt temporal answering grounding in medical instructional video. arXiv preprint arXiv:2203.06667 (2022)
Li, W., et al.: UNIMO: towards unified-modal understanding and generation via cross-modal contrastive learning. In: Meeting of the Association for Computational Linguistics (2020)
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: International Conference on Learning Representations (2018)
Malinowski, M., Fritz, M.: A multi-world approach to question answering about real-world scenes based on uncertain input. In: Neural Information Processing Systems (2014)
Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d’ Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32, Curran Associates Inc. (2019)
Peters, M.E., et al.: Deep contextualized word representations. In: North American Chapter of the Association for Computational Linguistics (2018)
Qi, D., Su, L., Song, J., Cui, E., Bharti, T., Sacheti, A.: ImageBERT: cross-modal pre-training with large-scale weak-supervised image-text data
Qiu, X.P., Sun, T.X., Xu, Y.G., Shao, Y.F., Dai, N., Huang, X.J.: Pre-trained models for natural language processing: a survey. Sci. China Technol. Sci. 63(10), 1872–1897 (2020). https://doi.org/10.1007/s11431-020-1647-3
Radford, A., Narasimhan, K.: Improving language understanding by generative pre-training
Ren, M., Kiros, R., Zemel, R.S.: Exploring models and data for image question answering. In: Neural Information Processing Systems (2015)
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)
Wang, P., et al.: Unifying architectures, tasks, and modalities through a simple sequence-to-sequence learning framework
Wang, W., Bao, H., Dong, L., Wei, F.: VLMo: unified vision-language pre-training with mixture-of-modality-experts. arXiv: 2111.02358 Computer Vision and Pattern Recognition (2021)
Wolf, T., et al.: Transformers: state-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations Association for Computational Linguistics, pp. 38–45 (2020)
Xu, R., et al.: Raise a child in large language model: towards effective and generalizable fine-tuning. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 9514–9528 (2021)
Xu, Y., Li, M., Cui, L., Huang, S., Wei, F., Zhou, M.: LayoutLM: pre-training of text and layout for document image understanding. knowledge discovery and data mining (2019)
Xu, Y., et al.: LayoutLMv2: multi-modal pre-training for visually-rich document understanding. In: Meeting of the Association for Computational Linguistics (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhu, M., Weng, Y., He, S., Liu, K., Zhao, J. (2022). Learning to Answer Complex Visual Questions from Multi-View Analysis. In: Zhang, N., Wang, M., Wu, T., Hu, W., Deng, S. (eds) CCKS 2022 - Evaluation Track. CCKS 2022. Communications in Computer and Information Science, vol 1711. Springer, Singapore. https://doi.org/10.1007/978-981-19-8300-9_17
Download citation
DOI: https://doi.org/10.1007/978-981-19-8300-9_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-8299-6
Online ISBN: 978-981-19-8300-9
eBook Packages: Computer ScienceComputer Science (R0)