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020 ▼a 9780438402140
035 ▼a (MiAaPQ)AAI10845004
035 ▼a (MiAaPQ)umd:19349
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0491 ▼f DP
0820 ▼a 004
1001 ▼a Singh, Bharat.
24510 ▼a Detecting Objects and Actions with Deep Learning.
260 ▼a [S.l.] : ▼b University of Maryland, College Park., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 115 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.
500 ▼a Adviser: Larry S. Davis.
5021 ▼a Thesis (Ph.D.)--University of Maryland, College Park, 2018.
520 ▼a Deep 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
520 ▼a In 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
520 ▼a Next, 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
520 ▼a Finally, 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
590 ▼a School code: 0117.
650 4 ▼a Computer science.
690 ▼a 0984
71020 ▼a University of Maryland, College Park. ▼b Computer Science.
7730 ▼t Dissertation Abstracts International ▼g 80-02B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0117
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000030 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자 ▼b 관리자