Mixup-Augmented Temporally Debiased Video Grounding with Content-Location Disentanglement

Author:

Wang Xin1ORCID,Wu Zihao1ORCID,Chen Hong1ORCID,Lan Xiaohan1ORCID,Zhu Wenwu1ORCID

Affiliation:

1. Tsinghua University, Beijing, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

ACM

Reference71 articles.

1. Learning Sample Importance for Cross-Scenario Video Temporal Grounding

2. Yoshua Bengio , Aaron Courville , and Pascal Vincent . 2013. Representation learning: A review and new perspectives . IEEE transactions on pattern analysis and machine intelligence, Vol. 35 , 8 ( 2013 ), 1798--1828. Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, Vol. 35, 8 (2013), 1798--1828.

3. Multi-Level Variational Autoencoder: Learning Disentangled Representations From Grouped Observations

4. Christopher P Burgess , Irina Higgins , Arka Pal , Loic Matthey , Nick Watters , Guillaume Desjardins , and Alexander Lerchner . 2018. Understanding disentangling in β-VAE. arXiv preprint arXiv:1804.03599 ( 2018 ). Christopher P Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner. 2018. Understanding disentangling in β-VAE. arXiv preprint arXiv:1804.03599 (2018).

5. Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset

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