通用物体识别是视觉场景理解的核心任务之一，近年来得益于深度学习技术的发展和互联网大数据的繁荣，取得了显著的进步，当前主流方法在一些封闭场景的数据集上甚至超越了人类视觉系统的识别能力。.本项目针对开放场景下大规模物体识别问题，以弱监督学习和迁移学习理论为基础，探索海量物体类别之间的复杂关联表示机理，研究大规模识别的高效可扩展分类理论，建立具有跨场景推广能力的视觉知识挖掘与迁移方法框架。.具体研究内容包括：针对复杂类间关联，研究符合人类感知机理的层级类别表示模型，提出语义属性嵌入的渐进式识别方法；分析数据长尾分布的统计特性，研究属性基元共享的跨类别零样本学习方法，建立真实场景下类别可扩展的开放式识别框架；研究场景与物体交互的视觉知识挖掘方法，构建具有自主更新与动态演化机制的视觉目标概念库，实现跨场景知识迁移引导下的大规模物体识别。.本项目预期取得理论创新与技术突破，促进视觉物体识别的实用推广。

Generic object recognition is one of the core tasks of visual scene understanding. In recent years, thanks to the development of deep learning technology and the prosperity of big Internet data, significant progress has been made. The current mainstream methods have even surpassed the performance of human beings visual system under some closed world settings..Aiming to tackle the problem of large scale object recognition in the open world, this project will take weakly supervised learning and transfer learning as its theoretical foundation, explore the complex correlation and representation mechanism between massive object categories, study the efficient and extensible classification theory for large scale recognition, and establish a framework of visual knowledge mining and transferring with the ability of cross scene generalization..Specifically, this project will address the following research issues. First, to characterize the complex cross-category correlation, we will study the hierarchical class representation model which conforms to the human perception mechanism, and propose a progressive recognition method with semantic attribute embedding. Second, we will conduct analysis on the statistical properties of the long tail distribution of data, study the cross-category zero-shot learning method based on shared attributes dictionary, and build an open world recognition framework with category scalability under realistic settings. Third, we will study the visual knowledge mining method that exploits the interaction between objects and scenes, construct a visual object concept database with self-update and dynamic evolution mechanism, and finally achieve appealing large scale object recognition under the guidance of cross-scene knowledge transfer..This project is expected to achieve both theoretical innovation and technological breakthroughs, and finally promote the practical application of visual object recognition.