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Multimodal reasoning is a challenging task that requires models to reason across multiple modalities to answer questions.
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C.L., Parikh, D.: Vqa: Visual question answering. In: Proceedings of the IEEE international conference on computer vision. pp. 2425–2433 (2015)
2015
Earlier work this paper cites.
Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28
2015
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
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)
2018
Earlier work this paper cites.
Kim, J.H., Jun, J., Zhang, B.T.: Bilinear attention networks. Advances in neural information processing systems 31
2018
Earlier work this paper cites.
Wu, C., Liu, J., Wang, X., Dong, X.: Chain of reasoning for visual question answering. Advances in Neural Information Processing Systems 31
2018
Earlier work this paper cites.
Gao, P., Jiang, Z., You, H., Lu, P., Hoi, S.C., Wang, X., Li, H.: Dynamic fusion with intra-and inter-modality attention flow for visual question answering. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 6639–6648 (2019)
2019
Earlier work this paper cites.
Kenton, J.D.M.W.C., Toutanova, L.K.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of naacL-HLT. vol. 1, p. 2 (2019)
2019
Earlier work this paper cites.
Lu, J., Batra, D., Parikh, D., Lee, S.: Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. Advances in neural information processing systems 32
2019
Earlier work this paper cites.
Tan, H., Bansal, M.: Lxmert: Learning cross-modality encoder representations from transformers. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (2019)
2019
Earlier work this paper cites.
Yu, Z., Yu, J., Cui, Y., Tao, D., Tian, Q.: Deep modular co-attention networks for visual question answering. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 6281–6290 (2019)
2019
Earlier work this paper cites.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33
2020
Earlier work this paper cites.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33
2020
Earlier work this paper cites.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33
2020
Earlier work this paper cites.
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: European conference on computer vision. pp. 213–229. Springer (2020)
2020
Earlier work this paper cites.
Khashabi, D., Min, S., Khot, T., Sabharwal, A., Tafjord, O., Clark, P., Hajishirzi, H.: UNIFIEDQA: Crossing format boundaries with a single QA system. In: Cohn, T., He, Y., Liu, Y. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2020. pp. 1896–1907. Association for Computational Linguistics, Online (Nov 2020). https://doi.org/10.18653/v1/2020.findings-emnlp.171, https://aclanthology.org/2020.findings-emnlp.171
2020
Earlier work this paper cites.
Li, L.H., Yatskar, M., Yin, D., Hsieh, C.J., Chang, K.W.: What does bert with vision look at? In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. pp. 5265–5275 (2020)
2020
Earlier work this paper cites.
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research 21
2020
Earlier work this paper cites.
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research 21
2020
Earlier work this paper cites.
2021
Earlier work this paper cites.
Kim, W., Son, B., Kim, I.: Vilt: Vision-and-language transformer without convolution or region supervision. In: International Conference on Machine Learning. pp. 5583–5594. PMLR (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Marino, K., Chen, X., Parikh, D., Gupta, A., Rohrbach, M.: Krisp: Integrating implicit and symbolic knowledge for open-domain knowledge-based vqa. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14111–14121 (2021)
2021
Cited alongside, same era.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
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Zhang, P., Li, X., Hu, X., Yang, J., Zhang, L., Wang, L., Choi, Y., Gao, J.: Vinvl: Revisiting visual representations in vision-language models. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 5579–5588 (2021)
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Kamath, A., Clark, C., Gupta, T., Kolve, E., Hoiem, D., Kembhavi, A.: Webly supervised concept expansion for general purpose vision models. In: European Conference on Computer Vision. pp. 662–681. Springer (2022)
2022
Cited alongside, same era.
Lu, P., Mishra, S., Xia, T., Qiu, L., Chang, K.W., Zhu, S.C., Tafjord, O., Clark, P., Kalyan, A.: Learn to explain: Multimodal reasoning via thought chains for science question answering. Advances in Neural Information Processing Systems 35
2022
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Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35
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