安装 Steam
登录
|
语言
繁體中文(繁体中文)
日本語(日语)
한국어(韩语)
ไทย(泰语)
български(保加利亚语)
Čeština(捷克语)
Dansk(丹麦语)
Deutsch(德语)
English(英语)
Español-España(西班牙语 - 西班牙)
Español - Latinoamérica(西班牙语 - 拉丁美洲)
Ελληνικά(希腊语)
Français(法语)
Italiano(意大利语)
Bahasa Indonesia(印度尼西亚语)
Magyar(匈牙利语)
Nederlands(荷兰语)
Norsk(挪威语)
Polski(波兰语)
Português(葡萄牙语 - 葡萄牙)
Português-Brasil(葡萄牙语 - 巴西)
Română(罗马尼亚语)
Русский(俄语)
Suomi(芬兰语)
Svenska(瑞典语)
Türkçe(土耳其语)
Tiếng Việt(越南语)
Українська(乌克兰语)
报告翻译问题

Jeff Donahue, Lisa Anne Hendricks, Marcus Rohrbach, Subhashini Venugopalan, Sergio Guadarrama, Kate Saenko, Trevor Darrell
(Submitted on 17 Nov 2014 (v1), last revised 31 May 2016 (this version, v4))
Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent, or "temporally deep", are effective for tasks involving sequences, visual and otherwise. We develop a novel recurrent convolutional architecture suitable for large-scale visual learning which is end-to-end trainable, and demonstrate the value of these models on benchmark video recognition tasks, image description and retrieval problems, and video narration challenges. In contrast to current
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:1411.4389 [cs.CV]
(or arXiv:1411.4389v4 [cs.CV] for this version)
Submission history
From: Jeff Donahue [view email]
[v1] Mon, 17 Nov 2014 08:25:17 GMT (577kb,D)
[v2] Tue, 18 Nov 2014 07:37:44 GMT (2700kb,D)
[v3] Tue, 17 Feb 2015 23:59:08 GMT (4430kb,D)
[v4] Tue, 31 May 2016 22:57:33 GMT (2346kb,D)
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
Link back to: arXiv, form interface, contact.