{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/temporal-segment-networks-towards-good","title":"Temporal Segment Networks: Towards Good Practices for Deep Action Recognition","arxiv_id":"1608.00859","date":"2016-08-02","proceeding":null,"authors":["Limin Wang","Yuanjun Xiong","Zhe Wang","Yu Qiao","Dahua Lin","Xiaoou Tang","Luc van Gool"],"abstract":"Deep convolutional networks have achieved great success for visual\nrecognition in still images. However, for action recognition in videos, the\nadvantage over traditional methods is not so evident. This paper aims to\ndiscover the principles to design effective ConvNet architectures for action\nrecognition in videos and learn these models given limited training samples.\nOur first contribution is temporal segment network (TSN), a novel framework for\nvideo-based action recognition. which is based on the idea of long-range\ntemporal structure modeling. It combines a sparse temporal sampling strategy\nand video-level supervision to enable efficient and effective learning using\nthe whole action video. The other contribution is our study on a series of good\npractices in learning ConvNets on video data with the help of temporal segment\nnetwork. Our approach obtains the state-the-of-art performance on the datasets\nof HMDB51 ( $ 69.4\\% $) and UCF101 ($ 94.2\\% $). We also visualize the learned\nConvNet models, which qualitatively demonstrates the effectiveness of temporal\nsegment network and the proposed good practices.","url_abs":"http://arxiv.org/abs/1608.00859v1","url_pdf":"http://arxiv.org/pdf/1608.00859v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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