向文本到图像合成Toward Text-to-Picture Synthesis |
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课程网址: | http://videolectures.net/nips09_goldberg_ttp/ |
主讲教师: | Andrew B. Goldberg |
开课单位: | 威斯康星大学 |
开课时间: | 2010-01-19 |
课程语种: | 英语 |
中文简介: | 据估计,美国有超过200万人患有严重的沟通障碍,导致他们依靠自然语言以外的方法进行沟通[2]。一种常用的增强和可选通信(AAC)系统是图形通信软件,如symwriter[8],它使用查找表将句子中的每个单词(或常用短语)翻译成图标。这是一个在模式之间转换信息的示例。然而,由此产生的图标序列可能难以理解。我们一直在开发通用文本到图片(TTP)合成算法[10,5],以使用机器学习技术提高可理解性。我们的目标是帮助有特殊需求的用户,如老年人或残疾人,通过图片摘要(如图5)快速浏览文档。我们的TTP系统以通用英语为目标。这不同于其他需要对场景[1,9]、三维模型[3]或特殊领域[6]进行手工叙述描述的图像转换系统。相反,我们使用串联或“拼贴”方法。在本文中,我们将讨论机器学习如何实现TTP系统的关键组件。 |
课程简介: | It is estimated that more that 2 million people in the United States have significant communication impairments that result in them relying on methods other than natural speech alone for communication [2]. One type of commonly used augmentative and alternative communication (AAC) system is pictorial communication software such as SymWriter [8], which uses a lookup table to transliterate each word (or common phrase) in a sentence into an icon. This is an example of converting information between modalities. However, the resulting sequence of icons can be difficult to understand. We have been developing general-purpose Text-to-Picture (TTP) synthesis algorithms [10, 5] to improve understandability using machine learning techniques. Our goal is to help users with special needs, such as the elderly or those with disabilities, to rapidly browse documents through pictorial summaries (e.g., Figure 5). Our TTP system targets general English. This differs from other pictorial conversion systems that require hand-crafted narrative descriptions of a scene [1, 9], 3D models [3], or special domains [6]. Instead, we use a concatenative or “collage” approach. In this talk, we discuss how machine learning enables the key components of our TTP system. |
关 键 词: | 机器学习; 文本图像合成; 语义 |
课程来源: | 视频讲座网 |
最后编审: | 2021-05-15:yumf |
阅读次数: | 70 |