Collaborative Research: Toward Category-Level Object Recognition

该项目解决了图像中类别级对象识别的问题：其目的是开发有效的方法来表示对象类;以半监督的方式从杂乱的样本图像中学习相应的对象模型;并有效和鲁棒地识别新图像中这些模型的实例，尽管杂乱，遮挡，视点和照明变化，以及每个类中的个体变化。智力优势。该项目的科学目标是开发一种对象的突出部分及其关系的表示，这种表示可以以弱监督的方式有效地从严重混乱的数据中学习，正确地捕获由于视点和照明变化而引起的类内变化和外观变化，并有效地支持对象模型的推理和高效分类机的自动构建。 该项目将通过国际学术和工业合作，研究类别级对象识别在图像检索、视频注释、人机交互、监控和安全以及机器人技术方面的应用。 对教育和外联的贡献将包括培训博士生和博士后研究人员，并让代表性不足的群体参与研究生研究和本科生数据收集以及经验评价项目。

This project addresses the problem of category-level object recognition in images: Its aim is to develop effective methodologies for representing object classes; learning the corresponding object models from cluttered sample images in a semi-supervised manner; and efficiently and robustly recognizing instances of these models in novel images despite clutter, occlusion, viewpoint and illumination changes, and individual variations within each class. Intellectual Merit. The scientific objective of this project is to develop a representation of the salient parts of an object and their relationships that can effectively be learned fromheavily cluttered data in a weakly supervised way, correctly captures within-class variability and appearance changes due to variations in viewpoint and illumination, and effectively supports inference over object models and the automated construction of efficient classification machines.Broader Impacts. This project will investigate applications of category-level object recognition to image retrieval, video annotation, human-computer interaction; surveillance and security; and robotics via international academic and industrial collaborations. Contributions to education and outreach will include training PhD students and post-doctoral researchers, and involving underrepresented groups in graduate research and undergraduate data collection and empirical evaluation projects.