Fetching the paper…
Reading the bibliography…
Recent video masked autoencoder (MAE) works have designed improved masking algorithms focused on saliency.
Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., Serre, T.: Hmdb: a large video database for human motion recognition. In: 2011 International conference on computer vision. pp. 2556–2563. IEEE (2011)
2011
Earlier work this paper cites.
2012
Earlier work this paper cites.
2016
Earlier work this paper cites.
Wang, L., Xiong, Y., Wang, Z., Qiao, Y., Lin, D., Tang, X., Van Gool, L.: Temporal segment networks: Towards good practices for deep action recognition. In: European conference on computer vision. pp. 20–36. Springer (2016)
2016
Earlier work this paper cites.
Goyal, R., Ebrahimi Kahou, S., Michalski, V., Materzynska, J., Westphal, S., Kim, H., Haenel, V., Fruend, I., Yianilos, P., Mueller-Freitag, M., et al.: The" something something" video database for learning and evaluating visual common sense. In: Proceedings of the IEEE international conference on computer vision. pp. 5842–5850 (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Van Den Oord, A., Vinyals, O., et al.: Neural discrete representation learning. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Damen, D., Doughty, H., Farinella, G.M., Fidler, S., Furnari, A., Kazakos, E., Moltisanti, D., Munro, J., Perrett, T., Price, W., Wray, M.: Scaling egocentric vision: The epic-kitchens dataset. In: European Conference on Computer Vision (ECCV) (2018)
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Li, Y., Li, Y., Vasconcelos, N.: Resound: Towards action recognition without representation bias. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 513–528 (2018)
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.
2018
Earlier work this paper cites.
Wang, L., Xiong, Y., Wang, Z., Qiao, Y., Lin, D., Tang, X., Van Gool, L.: Temporal segment networks for action recognition in videos. IEEE transactions on pattern analysis and machine intelligence 41
2018
Earlier work this paper cites.
Feichtenhofer, C., Fan, H., Malik, J., He, K.: Slowfast networks for video recognition. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 6202–6211 (2019)
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
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
Earlier work this paper cites.
Chen, M., Radford, A., Child, R., Wu, J., Jun, H., Luan, D., Sutskever, I.: Generative pretraining from pixels. In: International conference on machine learning. pp. 1691–1703. PMLR (2020)
2020
Earlier work this paper cites.
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International conference on machine learning. pp. 1597–1607. PMLR (2020)
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. In: International Conference on Learning Representations (2020)
2020
Cited alongside, same era.
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)
2020
Li, G., Zheng, H., Liu, D., Wang, C., Su, B., Zheng, C.: Semmae: Semantic-guided masking for learning masked autoencoders. Advances in Neural Information Processing Systems 35
2022
Later among the works it cites.
Li, J., Li, D., Xiong, C., Hoi, S.: Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In: International Conference on Machine Learning. pp. 12888–12900. PMLR (2022)
2022
Later among the works it cites.
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
Later among the works it cites.
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Hoffer, E., Ben-Nun, T., Hubara, I., Giladi, N., Hoefler, T., Soudry, D.: Augment your batch: Improving generalization through instance repetition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8129–8138 (2020)
2020
Cited alongside, same era.
Bao, H., Dong, L., Piao, S., Wei, F.: Beit: Bert pre-training of image transformers. In: International Conference on Learning Representations (2021)
2021
Cited alongside, same era.
Dwibedi, D., Aytar, Y., Tompson, J., Sermanet, P., Zisserman, A.: With a little help from my friends: Nearest-neighbor contrastive learning of visual representations. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 9588–9597 (2021)
2021
Cited alongside, same era.
Feichtenhofer, C., Fan, H., Xiong, B., Girshick, R., He, K.: A large-scale study on unsupervised spatiotemporal representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3299–3309 (2021)
2021
Cited alongside, same era.
Jia, C., Yang, Y., Xia, Y., Chen, Y.T., Parekh, Z., Pham, H., Le, Q., Sung, Y.H., Li, Z., Duerig, T.: Scaling up visual and vision-language representation learning with noisy text supervision. In: International conference on machine learning. pp. 4904–4916. PMLR (2021)
2021
Cited alongside, same era.
Qian, R., Meng, T., Gong, B., Yang, M.H., Wang, H., Belongie, S., Cui, Y.: Spatiotemporal contrastive video representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6964–6974 (2021)
2021
Cited alongside, same era.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
2021
Cited alongside, same era.
Recasens, A., Luc, P., Alayrac, J.B., Wang, L., Strub, F., Tallec, C., Malinowski, M., Pătrăucean, V., Altché, F., Valko, M., et al.: Broaden your views for self-supervised video learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 1255–1265 (2021)
2021
Cited alongside, same era.
Wang, J., Bertasius, G., Tran, D., Torresani, L.: Long-short temporal contrastive learning of video transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14010–14020 (2022)
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2023
Later among the works it cites.
Fan, D., Wang, J., Liao, S., Zhu, Y., Bhat, V., Santos-Villalobos, H., MV, R., Li, X.: Motion-guided masking for spatiotemporal representation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 5619–5629 (2023)
2023
Later among the works it cites.
Fan, D., Yang, D., Li, X., Bhat, V., Rohith, M.: Look globally and locally: Inter-intra contrastive learning from unlabeled videos. In: ICLR 2023 Workshop on Mathematical and Empirical Understanding of Foundation Models (2023)
2023
Later among the works it cites.
Huang, B., Zhao, Z., Zhang, G., Qiao, Y., Wang, L.: Mgmae: Motion guided masking for video masked autoencoding. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 13493–13504 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Li, Y., Fan, H., Hu, R., Feichtenhofer, C., He, K.: Scaling language-image pre-training via masking. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 23390–23400 (2023)
2023
Later among the works it cites.
Menon, S., Vondrick, C.: Visual classification via description from large language models. In: The Eleventh International Conference on Learning Representations (2023), https://openreview.net/forum?id=jlAjNL8z5cs
2023
Later among the works it cites.
Pratt, S., Covert, I., Liu, R., Farhadi, A.: What does a platypus look like? generating customized prompts for zero-shot image classification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 15691–15701 (2023)
2023
Later among the works it cites.
Wang, Y., He, Y., Li, Y., Li, K., Yu, J., Ma, X., Li, X., Chen, G., Chen, X., Wang, Y., et al.: Internvid: A large-scale video-text dataset for multimodal understanding and generation. In: The Twelfth International Conference on Learning Representations (2023)
2023
Later among the works it cites.