1. Brown, T., et al.: Language models are few-shot learners. In: Advances in Neural Information Processing Systems, vol. 33, pp. 1877–1901 (2020)

   Google Scholar 

2. Choi, H., Kim, J., Joe, S., Gwon, Y.: Evaluation of BERT and ALBERT sentence embedding performance on downstream NLP tasks. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 5482–5487 (2021).https://doi.org/10.1109/ICPR48806.2021.9412102

3. Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT (1), pp. 4171–4186. Association for Computational Linguistics (2019)

   Google Scholar 

4. Dosovitskiy, A., et al.: An image is worth\(16\times 16\) words: transformers for image recognition at scale. In: International Conference on Learning Representations (2021).https://openreview.net/forum?id=YicbFdNTTy

5. Gao, T., Yao, X., Chen, D.: SimCSE: simple contrastive learning of sentence embeddings. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 6894–6910 (2021)

   Google Scholar 

6. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778. IEEE Computer Society (2016)

   Google Scholar 

7. Huang, Z., Zeng, Z., Liu, B., Fu, D., Fu, J.: Pixel-BERT: aligning image pixels with text by deep multi-modal transformers. CoRR abs/2004.00849 (2020)

   Google Scholar 

8. Jia, C., et al.: Scaling up visual and vision-language representation learning with noisy text supervision. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning, ICML 2021, Virtual Event. Proceedings of Machine Learning Research, 18–24 July 2021, vol. 139, pp. 4904–4916. PMLR (2021)

   Google Scholar 

9. Li, X., et al.:Oscar: object-semantics aligned pre-training for vision-language tasks. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12375, pp. 121–137. Springer, Cham (2020).https://doi.org/10.1007/978-3-030-58577-8_8

   Chapter  Google Scholar 

10. Liu, H., Dai, Z., So, D., Le, Q.V.: Pay attention to MLPs. In: Thirty-Fifth Conference on Neural Information Processing Systems (2021).https://openreview.net/forum?id=KBnXrODoBW

11. Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. CoRR abs/1907.11692 (2019).http://arxiv.org/abs/1907.11692

12. Lu, J., Batra, D., Parikh, D., Lee, S.: ViLBERT: pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. In: Advances in Neural Information Processing Systems, vol. 32. Curran Associates, Inc. (2019).https://proceedings.neurips.cc/paper/2019/hash/c74d97b01eae257e44aa9d5bade97baf-Abstract.html

13. 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. CoRR abs/2001.07966 (2020)

    Google Scholar 

14. Radford, A., et al.: Learning transferable visual models from natural language supervision. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning, ICML 2021, Virtual Event. Proceedings of Machine Learning Research, 18–24 July 2021, vol. 139, pp. 8748–8763. PMLR (2021)

    Google Scholar 

15. Sariyildiz, M.B., Perez, J., Larlus, D.: Learning visual representations with caption annotations. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12353, pp. 153–170. Springer, Cham (2020).https://doi.org/10.1007/978-3-030-58598-3_10

    Chapter  Google Scholar 

16. Schönemann, P.: A generalized solution of the orthogonal procrustes problem. Psychometrika31(1), 1–10 (1966).https://doi.org/10.1007/BF02289451

    Article MathSciNet MATH  Google Scholar 

17. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May 2015, Conference Track Proceedings (2015).http://arxiv.org/abs/1409.1556

18. Su, W., et al.: VL-BERT: pre-training of generic visual-linguistic representations. In: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, 26–30 April 2020. OpenReview.net (2020)

    Google Scholar 

19. Sun, C., Shrivastava, A., Singh, S., Gupta, A.: Revisiting unreasonable effectiveness of data in deep learning era. CoRR abs/1707.02968 (2017).http://arxiv.org/abs/1707.02968

20. Tan, H., Bansal, M.: LXMERT: learning cross-modality encoder representations from transformers. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, 3–7 November 2019, pp. 5099–5110. Association for Computational Linguistics (2019)

    Google Scholar 

21. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

22. Young, P., Lai, A., Hodosh, M., Hockenmaier, J.: From image descriptions to visual denotations: new similarity metrics for semantic inference over event descriptions. Trans. Assoc. Comput. Linguist.2, 67–78 (2014)

    Article  Google Scholar 

23. Zhang, Y., Jiang, H., Miura, Y., Manning, C.D., Langlotz, C.P.: Contrastive learning of medical visual representations from paired images and text. CoRR abs/2010.00747 (2020)

    Google Scholar 

Download references