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Detecting Objects and Actions with Deep Learning

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개인저자Singh, Bharat.
단체저자명University of Maryland, College Park. Computer Science.
서명/저자사항Detecting Objects and Actions with Deep Learning.
발행사항[S.l.] : University of Maryland, College Park., 2018
발행사항Ann Arbor : ProQuest Dissertations & Theses, 2018
형태사항115 p.
소장본 주기School code: 0117.
ISBN9780438402140
일반주기 Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.
Adviser: Larry S. Davis.
요약Deep learning based visual recognition and localization is one of the pillars of computer vision and is the driving force behind applications like self-driving cars, visual search, video surveillance, augmented reality, to name a few. This thesi
요약In the first part, an analysis of different techniques for recognizing and detecting objects under extreme scale variation is presented. Since small and large objects are difficult to recognize at smaller and larger scales of an image pyramid re
요약Next, we present a real-time large-scale object detector (R-FCN-3000) for detecting thousands of classes where objectness detection and classification are decoupled. To obtain the detection score for an RoI, we multiply the objectness score with
요약Finally, we present a multi-stream bi-directional recurrent neural network for action detection. This was the first deep learning based system which could perform action localization in long videos and it could do it just with RGB data, without
일반주제명Computer science.
언어영어
기본자료 저록Dissertation Abstracts International80-02B(E).
Dissertation Abstract International
대출바로가기http://www.riss.kr/pdu/ddodLink.do?id=T15000030

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