Multi-Modal Masked Autoencoders for Medical Vision-and-Language Pre-Training

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.

Wu, Y., Schuster, M., Chen, Z., Le, Q.V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al.: Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016)

Original

2016

Earlier work this paper cites.

Yang, Z., He, X., Gao, J., Deng, L., Smola, A.: Stacked attention networks for image question answering. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 21–29 (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.

Yu, Z., Yu, J., Fan, J., Tao, D.: Multi-modal factorized bilinear pooling with co-attention learning for visual question answering. In: Proceedings of the IEEE international conference on computer vision. pp. 1821–1830 (2017)

2017

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.

Lau, J.J., Gayen, S., Abacha, A.B., Demner-Fushman, D.: A dataset of clinically generated visual questions and answers about radiology images. Scientific Data 5

2018

Earlier work this paper cites.

Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: International Conference on Learning Representations (2018)

2018

Earlier work this paper cites.

Pelka, O., Koitka, S., Rückert, J., Nensa, F., Friedrich, C.M.: Radiology objects in context (roco): a multimodal image dataset. In: Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, pp. 180–189. Springer (2018)

2018

Earlier work this paper cites.

Abacha, A.B., Hasan, S.A., Datla, V.V., Liu, J., Demner-Fushman, D., Müller, H.: Vqa-med: Overview of the medical visual question answering task at imageclef 2019. CLEF (Working Notes) 2

2019

Earlier work this paper cites.

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, Volume 1 (Long and Short Papers). pp. 4171–4186 (2019)

2019

Cited alongside, same era.

Li, L.H., Yatskar, M., Yin, D., Hsieh, C.J., Chang, K.W.: Visualbert: A simple and performant baseline for vision and language. arXiv preprint arXiv:1908.03557 (2019)

Original

2019

Cited alongside, same era.

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)

Original

2019

Cited alongside, same era.

Nguyen, B.D., Do, T.T., Nguyen, B.X., Do, T., Tjiputra, E., Tran, Q.D.: Overcoming data limitation in medical visual question answering. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 522–530. Springer (2019)

Dou, Z.Y., Xu, Y., Gan, Z., Wang, J., Wang, S., Wang, L., Zhu, C., Liu, Z., Zeng, M., et al.: An empirical study of training end-to-end vision-and-language transformers. arXiv preprint arXiv:2111.02387 (2021)

Original

2021

Later among the works it cites.

He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners. arXiv preprint arXiv:2111.06377 (2021)

Original

2021

Later among the works it cites.

Khare, Y., Bagal, V., Mathew, M., Devi, A., Priyakumar, U.D., Jawahar, C.: Mmbert: Multimodal bert pretraining for improved medical vqa. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). pp. 1033–1036. IEEE (2021)

2021

Later among the works it 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

Later among the works it cites.