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Vision and Language Pretraining has become the prevalent approach for tackling multimodal downstream tasks.
2010
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2015
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Karpathy, A., Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3128–3137 (2015)
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Plummer, B.A., Wang, L., Cervantes, C.M., Caicedo, J.C., Hockenmaier, J., Lazebnik, S.: Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models. In: Proceedings of the IEEE international conference on computer vision, pp. 2641–2649 (2015)
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Schuster, S., Krishna, R., Chang, A., Fei-Fei, L., Manning, C.D.: Generating semantically precise scene graphs from textual descriptions for improved image retrieval. In: Proceedings of the Fourth Workshop on Vision and Language, pp. 70–80, Association for Computational Linguistics, Lisbon, Portugal (Sep 2015), doi: 10.18653/v1/W15-2812
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Anderson, P., Fernando, B., Johnson, M., Gould, S.: Spice: Semantic propositional image caption evaluation. In: European conference on computer vision, pp. 382–398, Springer (2016)
2016
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Wu, Q., Shen, C., Liu, L., Dick, A., Van Den Hengel, A.: What value do explicit high level concepts have in vision to language problems? In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 203–212 (2016)
2016
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You, Q., Jin, H., Wang, Z., Fang, C., Luo, J.: Image captioning with semantic attention. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4651–4659 (2016)
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2017
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Goyal, Y., Khot, T., Summers-Stay, D., Batra, D., Parikh, D.: Making the v in vqa matter: Elevating the role of image understanding in visual question answering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6904–6913 (2017)
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Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.J., Shamma, D.A., et al.: Visual genome: Connecting language and vision using crowdsourced dense image annotations. International journal of computer vision 123
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Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision, pp. 618–626 (2017)
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Hjelm, R.D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Bachman, P., Trischler, A., Bengio, Y.: Learning deep representations by mutual information estimation and maximization. In: International Conference on Learning Representations (2018)
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Sharma, P., Ding, N., Goodman, S., Soricut, R.: Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In: ACL (2018)
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Yu, L., Lin, Z., Shen, X., Yang, J., Lu, X., Bansal, M., Berg, T.L.: Mattnet: Modular attention network for referring expression comprehension. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1307–1315 (2018)
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Bachman, P., Hjelm, R.D., Buchwalter, W.: Learning representations by maximizing mutual information across views. Advances in neural information processing systems 32
2019
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2019
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Ke, L., Pei, W., Li, R., Shen, X., Tai, Y.W.: Reflective decoding network for image captioning. In: Proceedings of the IEEE/CVF international conference on computer vision, pp. 8888–8897 (2019)
2019
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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
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Su, W., Zhu, X., Cao, Y., Li, B., Lu, L., Wei, F., Dai, J.: Vl-bert: Pre-training of generic visual-linguistic representations. In: International Conference on Learning Representations (2019)
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Suhr, A., Zhou, S., Zhang, A., Zhang, I., Bai, H., Artzi, Y.: A corpus for reasoning about natural language grounded in photographs. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 6418–6428, Association for Computational Linguistics, Florence, Italy (Jul 2019), doi: 10.18653/v1/P19-1644
2019
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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 and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 5100–5111, Association for Computational Linguistics, Hong Kong, China (Nov 2019), doi: 10.18653/v1/D19-1514
2019
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2019
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Xue, H., Huang, Y., Liu, B., Peng, H., Fu, J., Li, H., Luo, J.: Probing inter-modality: Visual parsing with self-attention for vision-and-language pre-training. Advances in Neural Information Processing Systems 34
2021
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2021
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Yu, F., Tang, J., Yin, W., Sun, Y., Tian, H., Wu, H., Wang, H.: Ernie-vil: Knowledge enhanced vision-language representations through scene graphs. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 3208–3216 (2021)
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Wu, H., Mao, J., Zhang, Y., Jiang, Y., Li, L., Sun, W., Ma, W.Y.: Unified visual-semantic embeddings: Bridging vision and language with structured meaning representations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)
2019
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2019
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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
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Chen, Y.C., Li, L., Yu, L., El Kholy, A., Ahmed, F., Gan, Z., Cheng, Y., Liu, J.: Uniter: Universal image-text representation learning. In: European conference on computer vision, pp. 104–120, Springer (2020)
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Gan, Z., Chen, Y.C., Li, L., Zhu, C., Cheng, Y., Liu, J.: Large-scale adversarial training for vision-and-language representation learning. Advances in Neural Information Processing Systems 33
2020
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He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 9729–9738 (2020)
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2020
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2020
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Lu, J., Goswami, V., Rohrbach, M., Parikh, D., Lee, S.: 12-in-1: Multi-task vision and language representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10437–10446 (2020)
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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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2022
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2022
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Bao, H., Dong, L., Piao, S., Wei, F.: BEit: BERT pre-training of image transformers. In: International Conference on Learning Representations (2022), URL https://openreview.net/forum?id=p-BhZSz59o4
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Couairon, G., Grechka, A., Verbeek, J., Schwenk, H., Cord, M.: Flexit: Towards flexible semantic image translation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18270–18279 (2022)
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Dalvi, F., Khan, A.R., Alam, F., Durrani, N., Xu, J., Sajjad, H.: Discovering latent concepts learned in BERT. In: International Conference on Learning Representations (2022), URL https://openreview.net/forum?id=POTMtpYI1xH
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2022
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Duan, J., Chen, L., Tran, S., Yang, J., Xu, Y., Zeng, B., Chilimbi, T.: Multi-modal alignment using representation codebook. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15651–15660 (2022)
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Hu, X., Gan, Z., Wang, J., Yang, Z., Liu, Z., Lu, Y., Wang, L.: Scaling up vision-language pre-training for image captioning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 17980–17989 (2022)
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Sammani, F., Mukherjee, T., Deligiannis, N.: Nlx-gpt: A model for natural language explanations in vision and vision-language tasks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8322–8332 (2022)
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