INT2-Medium: Understanding the meaning of images

INT2-Medium：理解图像的含义

• 批准号: 0803603
• 负责人: David Forsyth
• 金额: $ 55万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-15 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0803603&HistoricalAwards=false
• 关键词: INT2; Medium; Understanding; meaning; images

The ability to recognize objects in images is a core problem in computer vision. The last decade has seen astonishing advances in our methods to build object detectors. However, images convey richer information about the objects depicted in them: objects may form a scene ("A view of mountains and meadows"); objects are in relations with one another ("The cat sits on the mat"); different instances may look different ("The tabby cat sits on the blue mat"); objects may acting on others ("The cat is chasing the mouse"). This task of identifying the entities depicted in images, their attributes and relations is image understanding. This poses a number of new research questions: What objects should one remark on? What attributes of and relations between the objects depicted the image are important? That is, what is the visually salient information conveyed in an image?Many images (e.g. a large fraction of those on the web) are accompanied by text which describes or gives additional information about the entities depicted in them. The entities referred to in this text are typically visually salient ones. This correspondence between the information conveyed in the text and the image can be used in the creation of image understanding systems. Much current work treats image annotations that consist of individual words. The richer representations of meaning required to train image understanding systems can be obtained if annotating text is treated as sentences (rather than just bags of words). Sentences provide cues to: what is salient in an image; what salient objects likely look like (e.g. color, texture and form); and what relations might appear between them. Exposing this information will provide a rich body of training data for the next generation of computer vision systems.Research in natural language processing has created statistical wide- coverage parsers that can recover the semantic interpretation of sentences. These parsers differ from purely syntactic parsers in that they are based on linguistically expressive grammars that allow such interpretations to be built directly from the syntactic analysis. However, linking sentences with accompanying images requires a level of representation that goes beyond lists of the entities, states and events mentioned in a sentence. The writer of an image caption will typically assume that the reader sees the image, and can therefore refer to the entities depicted in it as known to the reader. There is a need parsers that are able to uncover the information structure of sentences -- what information is assumed to be shared knowledge between speaker and hearer, and what is new information asserted by the sentence. How information structure is encoded in natural language is well understood, and can be modeled with the same kinds of grammars that are used by those parsers that return semantic interpretations. Although there are currently no large corpora annotated with information structure, we will exploit the correspondence between images and their captions to develop novel, partially supervised, training regimes for parsers. These training regimes could also enable the bootstrapping of parsers for languages with no or little annotated training data.This project will build a novel parser that recovers richer linguistic representations, including information structure. It will build a novel image understanding system that recovers the salient entities depicted in an image together with their attributes and relations. The project will train these systems both separately on datasets consisting of sentences marked up with correct parses and images marked up with labels attached to objects, and jointly on a dataset of captioned images.Intellectual merits: The project goals are ambitious, but within reach, because both object recognition and parsing technology has advanced significantly. The project presents the vision and parsing communities with new goals, which are practically important and technically demanding. The aim of integrating natural language processing and computer vision creates a novel impetus to develop parsers that return richer linguistic representations, which will in turn have a deep impact on research within the natural language processing community itself. It will open up key directions in computer vision and natural language processing by demanding and enabling the recovery of richer representations of linguistic and visual information, and by studying how linguistic descriptions are grounded in the visual world.Broader impact: The project has significant practical implications in a number of areas such as image search, natural language interfaces for robotics, and will ultimately pave the way for new applications such as automatic captioning systems. The resulting advances in object recognition offer possibilities for the creation of safer autonomous vehicles, safer homes for better home care, and efficient management of surveillance data.URL: http://luthuli.cs.uiuc.edu/~daf/meaningofimages.html

图像中物体的识别能力是计算机视觉的核心问题。在过去的十年里，我们在建造物体探测器的方法上取得了惊人的进步。然而，图像传达了关于其中所描绘的物体的更丰富的信息：物体可以形成一个场景（“山和草地的景色”）；对象之间是有关系的（“猫坐在垫子上”）；不同的实例可能看起来不同（“虎斑猫坐在蓝色的垫子上”）；物体可能对其他物体起作用（“猫在追老鼠”）。这种识别图像中所描绘的实体及其属性和关系的任务就是图像理解。这就提出了一些新的研究问题：一个人应该评论什么对象？图像所描绘的对象的哪些属性和关系是重要的？也就是说，在图像中传达的视觉显著信息是什么？许多图像（例如，网络上的很大一部分图像）都附有文字，这些文字描述或给出了关于其中所描绘的实体的额外信息。本文中提到的实体通常是视觉上突出的实体。文本和图像所传达的信息之间的这种对应关系可以用于创建图像理解系统。目前的许多工作都是处理由单个单词组成的图像注释。如果将注释文本视为句子（而不仅仅是一袋单词），则可以获得训练图像理解系统所需的更丰富的意义表示。句子提供线索：图像中突出的部分；突出的物体可能是什么样子（例如颜色、纹理和形状）；以及它们之间可能出现的关系。揭示这些信息将为下一代计算机视觉系统提供丰富的训练数据。在自然语言处理方面的研究已经创造了统计广泛覆盖的解析器，可以恢复句子的语义解释。这些解析器与纯语法解析器的不同之处在于，它们基于语言表达性语法，允许直接从语法分析构建这样的解释。然而，将句子与附带的图像联系起来需要一定程度的表示，而不仅仅是句子中提到的实体、状态和事件的列表。图像标题的作者通常会假设读者看到图像，因此可以参考读者已知的图像中描述的实体。需要能够揭示句子信息结构的解析器——哪些信息被认为是说话者和听者之间的共享知识，哪些是句子断言的新信息。信息结构是如何用自然语言编码的已经很好地理解了，并且可以用那些返回语义解释的解析器所使用的相同类型的语法来建模。虽然目前没有带信息结构注释的大型语料库，但我们将利用图像及其标题之间的对应关系，为解析器开发新颖的、部分监督的训练机制。这些训练机制还可以为没有或很少带注释的训练数据的语言启动解析器。这个项目将建立一个新的解析器，恢复更丰富的语言表示，包括信息结构。它将建立一个新的图像理解系统，恢复图像中所描绘的显著实体及其属性和关系。该项目将分别训练这些系统的数据集，包括用正确解析标记的句子和用附加在物体上的标签标记的图像，以及联合训练这些系统的标题图像数据集。智力上的优点：项目的目标是雄心勃勃的，但是是可以实现的，因为对象识别和解析技术都有了显著的进步。该项目展示了具有新目标的愿景和解析社区，这些目标在实践中很重要，并且在技术上要求很高。整合自然语言处理和计算机视觉的目标为开发返回更丰富的语言表示的解析器创造了新的动力，这反过来将对自然语言处理社区本身的研究产生深远的影响。它将通过要求和实现语言和视觉信息的更丰富表示的恢复，以及通过研究语言描述如何以视觉世界为基础，开辟计算机视觉和自然语言处理的关键方向。更广泛的影响：该项目在许多领域具有重要的实际意义，如图像搜索、机器人的自然语言接口，并最终将为自动字幕系统等新应用铺平道路。物体识别方面的进步为创造更安全的自动驾驶汽车、更安全的家庭、更好的家庭护理和有效的监控数据管理提供了可能。URL: http://luthuli.cs.uiuc.edu/ daf / meaningofimages.html