@inproceedings {28317,
	title = {Reproducibility Companion Paper: Blind Natural Video Quality Prediction via Statistical Temporal Features and Deep Spatial Features},
	journal = {Proceedings of the 29th ACM International Conference on Multimedia (MM {\textquoteright}21)},
	year = {2021},
	pages = {3622{\textendash}3626},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {9781450386517},
	doi = {10.1145/3474085.3477933},
	url = {https://dl.acm.org/doi/10.1145/3474085.3477933},
	author = {Heng Tao {Shen} and Zhuang, Yueting and John R. {Smith} and Yang, Yang and Cesar, Pablo and Metze, Florian and Prabhakaran, Balakrishnan and Korhonen, Jari and Su, Yicheng and You, Junyong and Hicks, Steven and Midoglu, Cise}
}
@inproceedings {28321,
	title = {Reproducibility Companion Paper: Campus3D: A Photogrammetry Point Cloud Benchmark for Outdoor Scene Hierarchical Understanding},
	journal = {Proceedings of the 29th ACM International Conference on Multimedia (MM {\textquoteright}21)},
	year = {2021},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {9781450386517},
	doi = {10.1145/3474085.3477934},
	url = {https://dl.acm.org/doi/10.1145/3474085.3477934},
	author = {Heng Tao {Shen} and Zhuang, Yueting and John R. {Smith} and Yang, Yang and Cesar, Pablo and Metze, Florian and Prabhakaran, Balakrishnan and Liao, Yuqing and Li, Xinke and Tong, Zekun and Zhao, Yabang and Lim, Andrew and Kuang, Zhenzhong and Midoglu, Cise}
}
@inproceedings {28211,
	title = {Reproducibility Companion Paper: Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework},
	journal = {Proceedings of the 29th ACM International Conference on Multimedia (MM {\textquoteright}21)},
	year = {2021},
	pages = {3630{\textendash}3632},
	publisher = {ACM},
	address = {New York, NY, USA},
	abstract = {In this companion paper, we provide details of the artifacts to support the replication of "Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework", which was presented at MM{\textquoteright}20. The Inter-intra Contrastive (IIC) framework aims to extract more discriminative temporal information by extending intra-negative samples in contrastive self-supervised learning. In this paper, we first summarize our contribution. Then we explain the file structure of the source code and detailed settings. Since our proposal is a framework which contain a lot of different settings, we provide some custom settings to help other researchers to use our methods easily. The source code is available at https://github.com/BestJuly/IIC.},
	isbn = {9781450386517},
	doi = {10.1145/347408510.1145/3474085.3477939},
	url = {https://dl.acm.org/doi/proceedings/10.1145/3474085https://dl.acm.org/doi/10.1145/3474085.3477939https://dl.acm.org/doi/pdf/10.1145/3474085.3477939},
	author = {Heng Tao {Shen} and Zhuang, Yueting and John R. {Smith} and Yang, Yang and Cesar, Pablo and Metze, Florian and Prabhakaran, Balakrishnan and Tao, Li and Wang, Xueting and Yamasaki, Toshihiko and Chen, Jingjing and Hicks, Steven}
}
