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Learning Grammar of Complex Activities via Deep Neural Networks.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Learning Grammar of Complex Activities via Deep Neural Networks.
作者:	Mashaido, Becky.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2022
面頁冊數:	8 p.
附註:	Source: Masters Abstracts International, Volume: 82-09.
附註:	Advisor: Elhamifar, Ehsan.
Contained By:	Masters Abstracts International82-09.
標題:	Computer science.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28264699
ISBN:	9798582503200

Learning Grammar of Complex Activities via Deep Neural Networks.
Mashaido, Becky.

Learning Grammar of Complex Activities via Deep Neural Networks. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 8 p.

Source: Masters Abstracts International, Volume: 82-09.

Thesis (M.S.)--Northeastern University, 2022.

This item must not be sold to any third party vendors.

Motivated by the growing amount of publicly available video data on online streaming services and an increased interest in applications that analyze continuous video streams such as autonomous driving, this technical report provides a theoretical insight into deep neural networks for video learning, under label constraints. I build upon previous work in video learning for computer vision, make observations on model performance and propose further mechanisms to help improve our observations.

ISBN: 9798582503200Subjects--Topical Terms:

199325
Computer science.
Subjects--Index Terms:

Complex activities

Learning Grammar of Complex Activities via Deep Neural Networks.
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500 $a Source: Masters Abstracts International, Volume: 82-09.
500 $a Advisor: Elhamifar, Ehsan.
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506 $a This item must not be sold to any third party vendors.
520 $a Motivated by the growing amount of publicly available video data on online streaming services and an increased interest in applications that analyze continuous video streams such as autonomous driving, this technical report provides a theoretical insight into deep neural networks for video learning, under label constraints. I build upon previous work in video learning for computer vision, make observations on model performance and propose further mechanisms to help improve our observations.
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