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Detecting Objects and Actions with Deep Learning
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Detecting Objects and Actions with Deep Learning. |
| 개인저자 | Singh, Bharat. |
| 단체저자명 | University of Maryland, College Park. Computer Science. |
| 발행사항 | [S.l.]: University of Maryland, College Park., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 115 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-02B(E). Dissertation Abstract International |
| ISBN | 9780438402140 |
| 학위논문주기 | Thesis (Ph.D.)--University of Maryland, College Park, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.
Adviser: Larry S. Davis. |
| 요약 | 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 |
| 요약 | 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 |
| 요약 | 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 |
| 요약 | 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 |
| 일반주제명 | Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
