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Contrastive Language-Image Pre-training (CLIP) has been the cornerstone for zero-shot classification, text-image retrieval, and text-image generation by aligning image and text modalities.
Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR. pp. 248–255 (2009)
2009
Earlier work this paper cites.
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)
2009
Earlier work this paper cites.
Ordonez, V., Kulkarni, G., Berg, T.L.: Im2text: Describing images using 1 million captioned photographs. In: Shawe-Taylor, J., Zemel, R.S., Bartlett, P.L., Pereira, F.C.N., Weinberger, K.Q. (eds.) NeruIPS. pp. 1143–1151 (2011)
2011
Earlier work this paper cites.
Lin, T., Maire, M., Belongie, S.J., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft COCO: common objects in context. In: Fleet, D.J., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV. Lecture Notes in Computer Science, vol. 8693, pp. 740–755. Springer (2014)
2014
Earlier work this paper cites.
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. Linguistics 2
2014
Earlier work this paper cites.
Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L., Shamma, D.A., Bernstein, M.S., Fei-Fei, L.: Visual genome: Connecting language and vision using crowdsourced dense image annotations. Int. J. Comput. Vis. 123
2017
Earlier work this paper cites.
Recht, B., Roelofs, R., Schmidt, L., Shankar, V.: Do imagenet classifiers generalize to imagenet? In: Chaudhuri, K., Salakhutdinov, R. (eds.) ICML. Proceedings of Machine Learning Research, vol. 97, pp. 5389–5400. PMLR (2019)
2019
Earlier work this paper cites.
Changpinyo, S., Sharma, P., Ding, N., Soricut, R.: Conceptual 12M: Pushing web-scale image-text pre-training to recognize long-tail visual concepts. In: CVPR (2021)
2021
Earlier work this paper cites.
Hendrycks, D., Zhao, K., Basart, S., Steinhardt, J., Song, D.: Natural adversarial examples. In: CVPR. pp. 15262–15271. Computer Vision Foundation / IEEE (2021)
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I.: Learning transferable visual models from natural language supervision. In: Meila, M., Zhang, T. (eds.) ICML. Proceedings of Machine Learning Research, vol. 139, pp. 8748–8763. PMLR (2021)
2021
Earlier work this paper cites.
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models (2021)
2021
Earlier work this paper cites.
Xu, H., Ghosh, G., Huang, P., Okhonko, D., Aghajanyan, A., Metze, F., Zettlemoyer, L., Feichtenhofer, C.: Videoclip: Contrastive pre-training for zero-shot video-text understanding. In: Moens, M., Huang, X., Specia, L., Yih, S.W. (eds.) EMNLP. pp. 6787–6800. Association for Computational Linguistics (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Crowson, K., Biderman, S., Kornis, D., Stander, D., Hallahan, E., Castricato, L., Raff, E.: VQGAN-CLIP: open domain image generation and editing with natural language guidance. In: Avidan, S., Brostow, G.J., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV. Lecture Notes in Computer Science, vol. 13697, pp. 88–105. Springer (2022)
2022
Cited alongside, same era.
Frans, K., Soros, L.B., Witkowski, O.: Clipdraw: Exploring text-to-drawing synthesis through language-image encoders. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) NeurIPS (2022)
Zeng, Y., Zhang, X., Li, H., Wang, J., Zhang, J., Zhou, W.: X 2
2022
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Kim, D., Angelova, A., Kuo, W.: Region-aware pretraining for open-vocabulary object detection with vision transformers. In: CVPR. pp. 11144–11154. IEEE (2023)
2023
Later among the works it cites.
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2022
Cited alongside, same era.
Gu, X., Lin, T., Kuo, W., Cui, Y.: Open-vocabulary object detection via vision and language knowledge distillation. In: ICLR. OpenReview.net (2022)
2022
Cited alongside, same era.
Li, B., Weinberger, K.Q., Belongie, S.J., Koltun, V., Ranftl, R.: Language-driven semantic segmentation. In: ICLR. OpenReview.net (2022)
2022
Cited alongside, same era.
Li, Y., Liang, F., Zhao, L., Cui, Y., Ouyang, W., Shao, J., Yu, F., Yan, J.: Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm. In: ICLR. OpenReview.net (2022)
2022
Cited alongside, same era.
Luo, H., Ji, L., Zhong, M., Chen, Y., Lei, W., Duan, N., Li, T.: Clip4clip: An empirical study of CLIP for end to end video clip retrieval and captioning. Neurocomputing 508
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., Schramowski, P., Kundurthy, S., Crowson, K., Schmidt, L., Kaczmarczyk, R., Jitsev, J.: LAION-5B: an open large-scale dataset for training next generation image-text models. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) NeruIPS (2022)
2022
Cited alongside, same era.
Vinker, Y., Pajouheshgar, E., Bo, J.Y., Bachmann, R.C., Bermano, A.H., Cohen-Or, D., Zamir, A., Shamir, A.: Clipasso: semantically-aware object sketching. ACM Trans. Graph. 41
2022
Cited alongside, same era.
Xu, J., Mello, S.D., Liu, S., Byeon, W., Breuel, T.M., Kautz, J., Wang, X.: Groupvit: Semantic segmentation emerges from text supervision. In: CVPR. pp. 18113–18123. IEEE (2022)
2022
Cited alongside, same era.
Luo, Z., Zhao, P., Xu, C., Geng, X., Shen, T., Tao, C., Ma, J., Lin, Q., Jiang, D.: Lexlip: Lexicon-bottlenecked language-image pre-training for large-scale image-text sparse retrieval. In: IEEE/CVF International Conference on Computer Vision, ICCV 2023, Paris, France, October 1-6, 2023. pp. 11172–11183. IEEE (2023)
2023
Later among the works it cites.
OpenAI: GPT-4 technical report. CoRR abs/2303.08774
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Tang, Y., Yamada, Y., Zhang, Y., Yildirim, I.: When are lemons purple? the concept association bias of vision-language models. In: EMNLP. pp. 14333–14348. Association for Computational Linguistics (2023)
2023
Later among the works it cites.
Wang, J., Zhou, P., Shou, M.Z., Yan, S.: Position-guided text prompt for vision-language pre-training. In: CVPR. pp. 23242–23251. IEEE (2023)
2023
Later among the works it cites.
Su, J., Ahmed, M.H.M., Lu, Y., Pan, S., Bo, W., Liu, Y.: Roformer: Enhanced transformer with rotary position embedding. Neurocomputing 568
2024
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