Unsupervised Domain Adaptation for Video Object Grounding with Cascaded Debiasing Learning-Reference-Cited by-同舟云学术

Unsupervised Domain Adaptation for Video Object Grounding with Cascaded Debiasing Learning

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

Li Mengze¹^ORCID,Zhang Haoyu¹^ORCID,Li Juncheng¹^ORCID,Zhao Zhou²^ORCID,Zhang Wenqiao³^ORCID,Zhang Shengyu⁴^ORCID,Pu Shiliang⁵^ORCID,Zhuang Yueting¹^ORCID,Wu Fei⁶^ORCID

Affiliation:

1. Zhejiang University, Hangzhou, China

2. Zhejiang University & Shanghai Institute for Advanced Study of Zhejiang University, Hangzhou, China

3. National University of Singapore, Singapore, Singapore

4. School of Software Technology, Hangzhou, China

5. Hikvison, Hangzhou, China

6. Shanghai Institute for Advanced Study of Zhejiang University & Shanghai AI Laboratory, Hangzhou, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3581783.3612314

Reference59 articles.

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3. End-to-End Object Detection with Transformers

4. Junshen K Chen , Dallas Card , and Dan Jurafsky . 2022. Modular Domain Adaptation. arXiv preprint arXiv:2204.14213 ( 2022 ). Junshen K Chen, Dallas Card, and Dan Jurafsky. 2022. Modular Domain Adaptation. arXiv preprint arXiv:2204.14213 (2022).

5. Weakly-Supervised Spatio-Temporally Grounding Natural Sentence in Video