



Explore MovieNet
Data in MovieNet
Annotation in MovieNet
Benchmarks
 
							 
							 
							 
							 
							movienet-core contains multiple modules for processing
					the dataset, including:
					movienet-host is a flexiable MovieNet data manager
					that help you deploy MovieNet data server remotely or locally.
				movienet-tools, movienet, etc.
				Download MovieNet
MovieNet Projects
Holistic Movie Understanding Dataset
 
					Movie Cinematic Style Analysis
 
					Unsupervised Face Recognition
 
					Online Person Search
 
					Movie Scene Temporal Segmentation
 
					Movie Synopsis Association
 
					Person Search
 
					Person Recognition
 
					Trailer Analysis
 
					News
movienet-tools
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© 2020, OpenMMLab, by MMLab, CUHK. Last Update in July 2020.
@inproceedings{huang2020movienet,
	title={MovieNet: A Holistic Dataset for Movie Understanding},
	author={Huang, Qingqiu and Xiong, Yu and Rao, Anyi and Wang, Jiaze and Lin, Dahua},
	booktitle = {The European Conference on Computer Vision (ECCV)}, 
	year={2020}
}
@inproceedings{rao2020unified,
	title={A Unified Framework for Shot Type Classification Based on Subject Centric Lens},
	author={Rao, Anyi and Wang, Jiaze and Xu, Linning and Jiang, Xuekun and Huang, Qingqiu and Zhou, Bolei and Lin, Dahua},
	booktitle = {The European Conference on Computer Vision (ECCV)}, 
	year={2020}
}
@inproceedings{huang2020caption,
	title={Caption-Supervised Face Recognition: Training a State-of-the-Art Face Model without Manual Annotation},
	author={Huang, Qingqiu and Yang, Lei and Huang, Huaiyi and Wu, Tong and Lin, Dahua},
	booktitle = {The European Conference on Computer Vision (ECCV)}, 
	year={2020}
}
@inproceedings{xia2020online,
	title={Online Multi-modal Person Search in Videos},
	author={Xia, Jiangyue and Rao, Anyi and Xu, Linning and Huang, Qingqiu and Wen, Jiangtao and Lin, Dahua},
	booktitle = {The European Conference on Computer Vision (ECCV)}, 
	year={2020}
}
@inproceedings{rao2020local,
	title={A Local-to-Global Approach to Multi-modal Movie Scene Segmentation},
	author={Rao, Anyi and Xu, Linning and Xiong, Yu and Xu, Guodong and Huang, Qingqiu and Zhou, Bolei and Lin, Dahua},
	booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
	pages={10146--10155},
	year={2020}
}
@InProceedings{Xiong_2019_ICCV,
	author = {Xiong, Yu and Huang, Qingqiu and Guo, Lingfeng and Zhou, Hang and Zhou, Bolei and Lin, Dahua},
	title = {A Graph-Based Framework to Bridge Movies and Synopses},
	booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
	month = {October},
	year = {2019}
}
@inproceedings{huang2018person,
	title={Person Search in Videos with One Portrait Through Visual and Temporal Links},
	author={Huang, Qingqiu and Liu, Wentao and Lin, Dahua},
	booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
	pages={425--441},
	year={2018}
}
@InProceedings{Huang_2018_CVPR,
	author = {Huang, Qingqiu and Xiong, Yu and Lin, Dahua},
	title = {Unifying Identification and Context Learning for Person Recognition},
	booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	month = {June},
	year = {2018}
}
@article{huang2018trailers,
	title={From Trailers to Storylines: An Efficient Way to Learn from Movies},
	author={Huang, Qingqiu and Xiong, Yuanjun and Xiong, Yu and Zhang, Yuqi and Lin, Dahua},
	journal={arXiv preprint arXiv:1806.05341},
	year={2018}
}