| LDR | | 02660nam u200445 4500 |
| 001 | | 000000420894 |
| 005 | | 20190215164835 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438154162 |
| 035 | |
▼a (MiAaPQ)AAI10790412 |
| 035 | |
▼a (MiAaPQ)umd:18918 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 004 |
| 100 | 1 |
▼a Ng, Joe Yue-Hei. |
| 245 | 10 |
▼a Video Understanding with Deep Networks. |
| 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 130 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
| 500 | |
▼a Adviser: Larry S. Davis. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Maryland, College Park, 2018. |
| 520 | |
▼a Video understanding is one of the fundamental problems in computer vision. Videos provide more information to the image recognition task by adding a temporal component through which motion and other information can be additionally used. Encourag |
| 520 | |
▼a To effectively utilize deep networks, we need a comprehensive understanding of convolutional neural networks. We first study the network on the domain of image retrieval. We show that for instance-level image retrieval, lower layers often perfor |
| 520 | |
▼a We then propose and evaluate several deep neural network architectures to combine image information across a video over longer time periods than previously attempted. We propose two methods capable of handling full length videos. The first metho |
| 520 | |
▼a Next, we propose a multitask learning model ActionFlowNet to train a single stream network directly from raw pixels to jointly estimate optical flow while recognizing actions with convolutional neural networks, capturing both appearance and moti |
| 520 | |
▼a While recent deep models for videos show improvement by incorporating optical flow or aggregating high-level appearance across frames, they focus on modeling either the long-term temporal relations or short-term motion. We propose Temporal Diffe |
| 590 | |
▼a School code: 0117. |
| 650 | 4 |
▼a Computer science. |
| 650 | 4 |
▼a Artificial intelligence. |
| 690 | |
▼a 0984 |
| 690 | |
▼a 0800 |
| 710 | 20 |
▼a University of Maryland, College Park.
▼b Computer Science. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0117 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997567
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |