CLIP2TV: Align, Match and Distill for Video-Text Retrieval

Chen, D., Dolan, W.B.: Collecting highly parallel data for paraphrase evaluation. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies. pp. 190–200 (2011)

2011

Earlier work this paper cites.

Reed, S., Lee, H., Anguelov, D., Szegedy, C., Erhan, D., Rabinovich, A.: Training deep neural networks on noisy labels with bootstrapping. arXiv preprint arXiv:1412.6596 (2014)

Original

2014

Earlier work this paper cites.

Xu, J., Mei, T., Yao, T., Rui, Y.: Msr-vtt: A large video description dataset for bridging video and language. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 5288–5296 (2016)

2016

Earlier work this paper cites.

Anne Hendricks, L., Wang, O., Shechtman, E., Sivic, J., Darrell, T., Russell, B.: Localizing moments in video with natural language. In: Proceedings of the IEEE international conference on computer vision. pp. 5803–5812 (2017)

2017

Earlier work this paper cites.

Ghosh, A., Kumar, H., Sastry, P.: Robust loss functions under label noise for deep neural networks. In: Proceedings of the AAAI conference on artificial intelligence. vol. 31 (2017)

2017

Earlier work this paper cites.

Krishna, R., Hata, K., Ren, F., Fei-Fei, L., Carlos Niebles, J.: Dense-captioning events in videos. In: Proceedings of the IEEE international conference on computer vision. pp. 706–715 (2017)

2017

Earlier work this paper cites.

Li, Y., Yang, J., Song, Y., Cao, L., Luo, J., Li, L.J.: Learning from noisy labels with distillation. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 1910–1918 (2017)

2017

Earlier work this paper cites.

Patrini, G., Rozza, A., Krishna Menon, A., Nock, R., Qu, L.: Making deep neural networks robust to label noise: A loss correction approach. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1944–1952 (2017)

2017

Earlier work this paper cites.

Pereyra, G., Tucker, G., Chorowski, J., Kaiser, Ł., Hinton, G.: Regularizing neural networks by penalizing confident output distributions. arXiv preprint arXiv:1701.06548 (2017)

Original

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. Advances in neural information processing systems 30

2017

Earlier work this paper cites.

Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)

Original

2017

Earlier work this paper cites.

Han, B., Yao, Q., Yu, X., Niu, G., Xu, M., Hu, W., Tsang, I., Sugiyama, M.: Co-teaching: Robust training of deep neural networks with extremely noisy labels. Advances in neural information processing systems 31

2018

Earlier work this paper cites.

Hendrycks, D., Mazeika, M., Wilson, D., Gimpel, K.: Using trusted data to train deep networks on labels corrupted by severe noise. Advances in neural information processing systems 31

2018

Earlier work this paper cites.

Yu, Y., Kim, J., Kim, G.: A joint sequence fusion model for video question answering and retrieval. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 471–487 (2018)

2018

Earlier work this paper cites.

Zhang, B., Hu, H., Sha, F.: Cross-modal and hierarchical modeling of video and text. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 374–390 (2018)

2018

Earlier work this paper cites.

Zhang, Z., Sabuncu, M.: Generalized cross entropy loss for training deep neural networks with noisy labels. Advances in neural information processing systems 31

2018

Earlier work this paper cites.

Arazo, E., Ortego, D., Albert, P., O’Connor, N., McGuinness, K.: Unsupervised label noise modeling and loss correction. In: International conference on machine learning. pp. 312–321. PMLR (2019)

2019

Earlier work this paper cites.

Liu, Y., Albanie, S., Nagrani, A., Zisserman, A.: Use what you have: Video retrieval using representations from collaborative experts. arXiv preprint arXiv:1907.13487 (2019)

Original

2019

Cited alongside, same era.

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

Cited alongside, same era.

Miech, A., Zhukov, D., Alayrac, J.B., Tapaswi, M., Laptev, I., Sivic, J.: Howto100m: Learning a text-video embedding by watching hundred million narrated video clips. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 2630–2640 (2019)

2019

Cited alongside, same era.

Song, H., Kim, M., Lee, J.G.: Selfie: Refurbishing unclean samples for robust deep learning. In: International Conference on Machine Learning. pp. 5907–5915. PMLR (2019)

2019

Cited alongside, same era.

Bain, M., Nagrani, A., Varol, G., Zisserman, A.: Frozen in time: A joint video and image encoder for end-to-end retrieval. arXiv preprint arXiv:2104.00650 (2021)

Original

2021

Closest in time.

Dzabraev, M., Kalashnikov, M., Komkov, S., Petiushko, A.: Mdmmt: Multidomain multimodal transformer for video retrieval. arXiv preprint arXiv:2103.10699 (2021)

Original

2021

Closest in time.

Fang, H., Xiong, P., Xu, L., Chen, Y.: Clip2video: Mastering video-text retrieval via image clip. arXiv preprint arXiv:2106.11097 (2021)

Original

2021

Closest in time.

Hu, P., Peng, X., Zhu, H., Zhen, L., Lin, J.: Learning cross-modal retrieval with noisy labels. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5403–5413 (2021)

2021

Closest in time.