| LDR | | 00000nmm u2200205 4500 |
| 001 | | 000000333294 |
| 005 | | 20250110094025 |
| 008 | | 181129s2018 ||| | | | eng d |
| 020 | |
▼a 9780438026872 |
| 035 | |
▼a (MiAaPQ)AAI10815847 |
| 035 | |
▼a (MiAaPQ)cornellgrad:10825 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 049 | 1 |
▼f DP |
| 082 | 0 |
▼a 004 |
| 100 | 1 |
▼a Veit, Andreas. |
| 245 | 10 |
▼a Learning Conditional Models for Visual Perception. |
| 260 | |
▼a [S.l.] :
▼b Cornell University.,
▼c 2018 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
| 300 | |
▼a 125 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B. |
| 500 | |
▼a Adviser: Serge J. Belongie. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Cornell University, 2018. |
| 520 | |
▼a In recent years, the field of computer vision has seen a series of major advances, made possible by rapid development in algorithms, data collection and computing infrastructure. As a result, vision systems have started to be broadly adopted in |
| 520 | |
▼a In this dissertation, we address this limitation by building conditional vision models that can learn from multiple points of view and adapt their results to account for different conditions. First, we address the related tasks of image tagging |
| 590 | |
▼a School code: 0058. |
| 650 | 4 |
▼a Computer science. |
| 690 | |
▼a 0984 |
| 710 | 20 |
▼a Cornell University.
▼b Computer Science. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-10B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0058 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998204
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자
▼b 관리자 |