CAREER: Situated Recognition: Learning to understand our local visual environment

职业：情境识别：学习了解我们当地的视觉环境

• 批准号: 1452851
• 负责人: Alexander Berg
• 金额: $ 51万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-03-01 至 2020-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1452851&HistoricalAwards=false
• 关键词: CAREER; Situated; Recognition; Learning; understand

This project develops computer vision technologies for recognizing objects in our daily lives. For recognizing visual content around us, where cameras can record multiple images over a period of time, there is an opportunity to take advantage of context that is not available for internet images. This project pursues new representations and computational strategies exploiting this context efficiently to achieve high-quality visual recognition in our environment. Balanced against the opportunity of using this context is the challenge of making recognition work in any particular environment, in the face of clutter, occlusion, non-canonical views, and idiosyncratic appearance variation. The methods developed can be a core part of developing technology to help computer vision systems scale to recognize everything in our daily world. The research leads to automated systems for better understanding and monitoring of our daily environment, improved human-computer interaction, and encourages more research in this area.This research direction is different from the majority of work in recognition that has focused on internet images collected from the web. The biases of such web-collected may lead to models that do not generalize to a particular environment. Situated recognition allows exploiting local context, including human interaction and spoken language, to build models specific to an environment and furthermore to the parts of an environment that are important to people. The project collects multiple datasets stressing multi-view imagery and long-term observation of environments while sampling a wide variety of settings. The research team develops algorithms to parse and detect objects by exploiting context, efficient re-use, and context-dependent saliency; and uses situated natural language to drive automatic learning of visual recognition models.Project Webpage: http://acberg.com

该项目开发了计算机视觉技术，以识别我们日常生活中的对象。 为了识别我们周围的视觉内容，相机可以在一段时间内录制多个图像，有机会利用上下文，而这些上下文是不可用于Internet图像的上下文。 该项目追求新的表示和计算策略，以有效利用这种环境，以在我们的环境中实现高质量的视觉识别。 面对在任何特定环境中识别识别工作的挑战，面对混乱，遮挡，非经典观点和特质外观变化，与使用这种情况的机会保持平衡。 开发的方法可能是开发技术的核心部分，以帮助计算机视觉系统规模识别我们日常世界中的一切。该研究导致自动化系统，以更好地理解和监测我们的日常环境，改善人类计算机的互动，并鼓励在这一领域进行更多的研究。这一研究方向与认可的大多数工作不同，该工作集中在网络上收集的互联网图像上。此类网络收集的偏见可能导致不推广到特定环境的模型。位置认可允许利用包括人类互动和口语在内的当地环境，以构建特定环境的模型，并构建对人们重要的环境的部分。该项目收集了多个数据集，这些数据集强调多视图图像和对环境的长期观察，同时采样了各种各样的设置。研究团队通过利用上下文，有效的重复使用和上下文依赖性显着性来开发算法来解析和检测对象。并使用自然语言来推动自动学习视觉识别模型。项目网页：http：//acberg.com