Video Infringement Detection via Feature Disentanglement and Mutual Information Maximization-Reference-Cited by-同舟云学术

Video Infringement Detection via Feature Disentanglement and Mutual Information Maximization

Published:2023-10-26 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 31st ACM International Conference on Multimedia
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Author:

Liu Zhenguang¹^ORCID,Yu Xinyang²^ORCID,Wang Ruili³^ORCID,Ye Shuai¹^ORCID,Ma Zhe¹^ORCID,Dong Jianfeng²^ORCID,He Sifeng⁴^ORCID,Qian Feng⁴^ORCID,Zhang Xiaobo⁴^ORCID,Zimmermann Roger⁵^ORCID,Yang Lei⁴^ORCID

Affiliation:

1. Zhejiang University, Hangzhou, China

2. Zhejiang Gongshang University, Hangzhou, China

3. Massey University, Auckland, New Zealand

4. Ant Group, Hangzhou, China

5. National University of Singapore, Singapore, Singapore

Funder

Key R&D Program of Zhejiang Province

R&D Program of DCI Technology and Application Joint Laboratory

CCF-AFSG Research Fund

Zhejiang Gongshang University Digital+ Disciplinary Construction Management Project

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3581783.3612002

Reference44 articles.

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2. Million-scale near-duplicate video retrieval system

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4. Ting Chen , Simon Kornblith , Mohammad Norouzi , and Geoffrey Hinton . 2020 . A simple framework for contrastive learning of visual representations . In International conference on machine learning. PMLR, 1597--1607 . Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597--1607.

5. Xinlei Chen*, Saining Xie*, and Kaiming He. 2021. An Empirical Study of Training Self-Supervised Vision Transformers. arXiv preprint arXiv:2104.02057 ( 2021 ). Xinlei Chen*, Saining Xie*, and Kaiming He. 2021. An Empirical Study of Training Self-Supervised Vision Transformers. arXiv preprint arXiv:2104.02057 (2021).