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

该项目开发计算机视觉技术，用于识别我们日常生活中的物体。 为了识别我们周围的视觉内容，相机可以在一段时间内记录多个图像，有机会利用互联网图像无法利用的上下文。 该项目追求新的表示和计算策略，有效地利用这一背景，以实现高质量的视觉识别在我们的环境中。 与使用这种上下文的机会相平衡的是，在任何特定环境中，面对混乱、遮挡、非规范视图和特殊外观变化时，使识别工作的挑战。 开发的方法可以成为开发技术的核心部分，以帮助计算机视觉系统扩展到识别我们日常世界中的一切。这项研究导致了自动化系统，以更好地了解和监测我们的日常环境，改善人机交互，并鼓励更多的研究在这一领域。这一研究方向是不同的，从网络上收集的互联网图像集中在识别的大部分工作。这种网络收集的偏见可能会导致模型不能推广到特定的环境。情境识别允许利用本地上下文，包括人类交互和口语，来构建特定于环境以及环境中对人们重要的部分的模型。该项目收集了多个数据集，强调多视图图像和对环境的长期观察，同时对各种环境进行采样。该研究团队开发算法，通过利用上下文、高效重用和上下文相关显著性来解析和检测对象;并使用情境自然语言来驱动视觉识别模型的自动学习。项目网页：http://acberg.com

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