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Most of the currently existing vision and language pre-training (VLP) methods have mainly focused on how to extract and align vision and text features.
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Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C.L., Parikh, D.: Vqa: Visual question answering. In: ICCV (2015)
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Karpathy, A., Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions. In: CVPR (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: ICCV (2015)
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Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: A unified embedding for face recognition and clustering. In: CVPR (2015)
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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: CVPR (2017)
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Harwood, B., Kumar BG, V., Carneiro, G., Reid, I., Drummond, T.: Smart mining for deep metric learning. In: ICCV (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. IJCV (2017)
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Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A.: Automatic differentiation in pytorch. In: NeurIPS Workshops (2017)
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: NeurIPS (2017)
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Wu, C.Y., Manmatha, R., Smola, A.J., Krahenbuhl, P.: Sampling matters in deep embedding learning. In: ICCV (2017)
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Kim, J.H., Jun, J., Zhang, B.T.: Bilinear attention networks. In: NeurIPS (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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Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: CVPR (2018)
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2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
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Wang, X., Zhang, H., Huang, W., Scott, M.R.: Cross-batch memory for embedding learning. In: CVPR (2020)
2020
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2020
Later among the works it cites.
Xuan, H., Stylianou, A., Liu, X., Pless, R.: Hard negative examples are hard, but useful. In: ECCV (2020)
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Xuan, H., Stylianou, A., Pless, R.: Improved embeddings with easy positive triplet mining. In: WACV (2020)
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2021
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2019
Cited alongside, same era.
2019
Cited alongside, same era.
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: ECCV (2020)
2020
Cited alongside, same era.
Chuang, C.Y., Robinson, J., Lin, Y.C., Torralba, A., Jegelka, S.: Debiased contrastive learning. In: NeurIPS (2020)
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Gan, Z., Chen, Y.C., Li, L., Zhu, C., Cheng, Y., Liu, J.: Large-scale adversarial training for vision-and-language representation learning. In: NeurIPS (2020)
2020
Cited alongside, same era.
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: CVPR (2020)
2020
Cited alongside, same era.
Later among the works it cites.
2021
Later among the works it cites.
Huang, Z., Zeng, Z., Huang, Y., Liu, B., Fu, D., Fu, J.: Seeing out of the box: End-to-end pre-training for vision-language representation learning. In: CVPR (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
Li, J., Selvaraju, R., Gotmare, A., Joty, S., Xiong, C., Hoi, S.C.H.: Align before fuse: Vision and language representation learning with momentum distillation. In: NeurIPS (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., Jégou, H.: Training data-efficient image transformers & distillation through attention. In: ICML (2021)
2021
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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. In: NeurIPS (2021)
2021
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Zolfaghari, M., Zhu, Y., Gehler, P., Brox, T.: Crossclr: Cross-modal contrastive learning for multi-modal video representations. In: ICCV (2021)
2021
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2022
Closest in time.
Yang, J., Duan, J., Tran, S., Xu, Y., Chanda, S., Chen, L., Zeng, B., Chilimbi, T., Huang, J.: Vision-language pre-training with triple contrastive learning. In: CVPR (2022)
2022
Closest in time.