Frozen CLIP Models are Efficient Video Learners

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.

Soomro, K., Zamir, A.R., Shah, M.: Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402 (2012)

Original

2012

Earlier work this paper cites.

Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. arXiv preprint arXiv:1406.2199 (2014)

Original

2014

Earlier work this paper cites.

Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3d convolutional networks. In: Proceedings of the IEEE international conference on computer vision. pp. 4489–4497 (2015)

2015

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.

Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 6299–6308 (2017)

2017

Earlier work this paper cites.

Hara, K., Kataoka, H., Satoh, Y.: Learning spatio-temporal features with 3d residual networks for action recognition. In: Proceedings of the IEEE International Conference on Computer Vision Workshops. pp. 3154–3160 (2017)

2017

Earlier work this paper cites.

Qiu, Z., Yao, T., Mei, T.: Learning spatio-temporal representation with pseudo-3d residual networks. In: proceedings of the IEEE International Conference on Computer Vision. pp. 5533–5541 (2017)

2017

Earlier work this paper cites.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Advances in neural information processing systems. pp. 5998–6008 (2017)

2017

Earlier work this paper cites.

Diba, A., Fayyaz, M., Sharma, V., Arzani, M.M., Yousefzadeh, R., Gall, J., Van Gool, L.: Spatio-temporal channel correlation networks for action classification. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 284–299 (2018)

2018

Earlier work this paper cites.

Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., Paluri, M.: A closer look at spatiotemporal convolutions for action recognition. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. pp. 6450–6459 (2018)

2018

Earlier work this paper cites.

Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7794–7803 (2018)

2018

Earlier work this paper cites.

Xie, S., Sun, C., Huang, J., Tu, Z., Murphy, K.: Rethinking spatiotemporal feature learning: Speed-accuracy trade-offs in video classification. In: Proceedings of the European conference on computer vision (ECCV). pp. 305–321 (2018)

2018

Earlier work this paper cites.

Zhou, B., Andonian, A., Oliva, A., Torralba, A.: Temporal relational reasoning in videos. In: Proceedings of the European conference on computer vision (ECCV). pp. 803–818 (2018)

2018

Earlier work this paper cites.

Zolfaghari, M., Singh, K., Brox, T.: Eco: Efficient convolutional network for online video understanding. In: Proceedings of the European conference on computer vision (ECCV). pp. 695–712 (2018)

2018

Earlier work this paper cites.

Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT (2019)

2019

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.

Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., Attariyan, M., Gelly, S.: Parameter-efficient transfer learning for nlp. In: International Conference on Machine Learning. pp. 2790–2799. PMLR (2019)

2019

Earlier work this paper cites.

Tran, D., Wang, H., Torresani, L., Feiszli, M.: Video classification with channel-separated convolutional networks. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 5552–5561 (2019)

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.

Duan, H., Zhao, Y., Xiong, Y., Liu, W., Lin, D.: Omni-sourced webly-supervised learning for video recognition. In: European Conference on Computer Vision. pp. 670–688. Springer (2020)

2020

Earlier work this paper cites.

Feichtenhofer, C.: X3d: Expanding architectures for efficient video recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 203–213 (2020)

2020

Cited alongside, same era.

Guo, D., Rush, A.M., Kim, Y.: Parameter-efficient transfer learning with diff pruning. arXiv preprint arXiv:2012.07463 (2020)

Original

2020

Cited alongside, same era.

Pfeiffer, J., Kamath, A., Rücklé, A., Cho, K., Gurevych, I.: Adapterfusion: Non-destructive task composition for transfer learning. arXiv preprint arXiv:2005.00247 (2020)

Original

2020

Cited alongside, same era.

Zhu, L., Tran, D., Sevilla-Lara, L., Yang, Y., Feiszli, M., Wang, H.: Faster recurrent networks for efficient video classification. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 13098–13105 (2020)

2020

Cited alongside, same era.

Liu, Z., Ning, J., Cao, Y., Wei, Y., Zhang, Z., Lin, S., Hu, H.: Video swin transformer. arXiv preprint arXiv:2106.13230 (2021)

Original

2021

Later among the works it cites.

Patrick, M., Campbell, D., Asano, Y.M., Metze, I.M.F., Feichtenhofer, C., Vedaldi, A., Henriques, J.F.: Keeping your eye on the ball: Trajectory attention in video transformers. In: Advances in Neural Information Processing Systems (NeurIPS) (2021)

2021

Later among the works it cites.

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. arXiv preprint arXiv:2103.00020 (2021)

Original

2021

Later among the works it cites.

Riquelme, C., Puigcerver, J., Mustafa, B., Neumann, M., Jenatton, R., Pinto, A.S., Keysers, D., Houlsby, N.: Scaling vision with sparse mixture of experts. arXiv preprint arXiv:2106.05974 (2021)

Original

2021

Later among the works it cites.