AuthorsH. T. Shen, Y. Zhuang, J. R. Smith, Y. Yang, P. Cesar, F. Metze, B. Prabhakaran, L. Tao, X. Wang, T. Yamasaki et al.
TitleReproducibility Companion Paper: Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework
AfilliationMachine Learning
Project(s)Department of Holistic Systems
StatusPublished
Publication TypeProceedings, refereed
Year of Publication2021
Conference NameProceedings of the 29th ACM International Conference on Multimedia (MM '21)
Pagination3630–3632
PublisherACM
Place PublishedNew York, NY, USA
ISBN Number9781450386517
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'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.
URLhttps://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
DOI10.1145/347408510.1145/3474085.3477939
Citation Key28211