Incremental Learning of Object Categories

复杂视觉场景下的目标自动识别仍然是计算机视觉领域最活跃的研究领域之一。然而，目前可用的大多数系统只能解决具有固定数量的已知对象类的非常特定的问题(目前在100到1000个之间)。该建议通过开发方法来克服静态系统的这些限制，该方法通过增量地增加系统的知识来适应最初从固定数据集训练的系统。随着时间的推移，我们将这种适应系统知识的过程称为“增量学习”或“终身学习”。这种方法基本上用于识别和表示以前未见过的类别，特别是在诸如Flickr之类的万维网上公开可用的图像集合的时代。该项目的一个长期目标是缩小人类和机器视觉之间的差距，这可以通过建立能够自动检测新的对象类别并将其添加到当前数据集中的系统来实现。调整目前的表示方式将是这一进程中涉及的一个重要议题，以确保不同类别的可分性，即使已知的类别有数千个。在项目的第一阶段，新颖性检测和从少数几个例子中学习将是研究的重点。此外，还需要研究在特征级别和对象实例级别上有效更新当前模型的方法。因此，可以避免昂贵的从头开始学习。此外，主动学习是一个高度相关的主题，以确保新示例与所产生的分类模型的相关性。由于在这一研究领域内没有共同的基准数据集，我们计划建立一个数据集，并为将向公众提供的终身学习情景确定评估标准。作为一个真实世界的应用，我们将在移动机器人上评估所开发的方法，该方法将自主地探索其环境，并将增量地扩展其知识。这一提议的长期目标是开发方法，通过这些方法，系统能够自动适应每日变化的世界，并随着时间的推移增加和纠正其知识。

Automatic object recognition in complex visual scenes is still one of the most active research areas in computer vision. However, the majority of currently available systems are only capable of solving very specific problems with a fixed number of known object classes (currently between 100 and 1000). This proposal overcomes these limitations of static systems by developing methods, which adapt a system initially trained from a fixed dataset by incrementally increasing its knowledge. We call this process of adapting a systems knowledge over time "incremental learning" or "lifelong learning". Such methods are essentially for recognizing and representing previously unseen categories, especially in the time of publicly available image collections on the world wide web such as Flickr. A long term goal of this project is to close the gap between human and machine vision, which can be done by building systems that are able to automatically detect new object categories and to add them to their current datasets. Adapting the current representation modalities will be one important topic involved within this process to ensure the separability of different categories even with thousands of known classes. In the first stage of the project, novelty detection and learning from few examples will be in the focus of research. In addition, methods for efficiently updating the current model need to be investigated, both on the level of features and on the level of object instances. Thereby, costly learning from scratch can be avoided. Furthermore, active learning is a topic highly relevant to ensure the relevance of new examples with respect to the resulting classification model. Since there is no common benchmark dataset within this area of research, we plan to build a dataset and to define evaluation criteria for lifelong learning scenarios which will be made publicly available. As an real-world application, we will evaluate the developed methods on a mobile robot, that will autonomously explore its environment and will incrementally extend its knowledge. The long term goal of this proposal is to develop methods, with which a system is able to automatically adapt itself to the daily changing world and to increase and correct its knowledge over time.