知识永远不够：网络辅助深度开放世界识别
Knowledge is Never Enough: Towards Web Aided Deep Open World Recognition
摘要：
虽然今天的机器人能够执行复杂的任务，但它们只能对经过训练的物体采取行动。这是一个严重的限制：任何机器人都不可避免地会在不受约束的环境中看到新的物体，因此总是会有视觉知识缺口。然而，标准的可视化模块通常构建在有限的类集合上，并且基于对象必须属于这些类之一的强先验。识别一个实例是否不属于已知类别的集合（即开集识别），只是部分地解决了这个问题，因为一个真正的自主智能体不仅应该能够检测到它不知道的东西，而且还应该动态地扩展它对世界的知识。我们通过一个深度学习架构来应对这一挑战，该架构可以以端到端的方式动态更新其已知类。所提出的深度网络基于非参数模型的深度扩展，可以检测感知到的对象是否属于系统已知的类别集，并在无需从头开始重新训练整个系统的情况下进行学习。关于新类别的注释图像可以由“oracle”（即人类监督）或Web的自主挖掘提供。两个不同的数据库和机器人平台上的实验证明了我们的方法的承诺。
英文摘要：
While today's robots are able to perform sophisticated tasks, they can only act on objects they have been trained to recognize. This is a severe limitation: any robot will inevitably see new objects in unconstrained settings, and thus will always have visual knowledge gaps. However, standard visual modules are usually built on a limited set of classes and are based on the strong prior that an object must belong to one of those classes. Identifying whether an instance does not belong to the set of known categories (i.e. open set recognition), only partially tackles this problem, as a truly autonomous agent should be able not only to detect what it does not know, but also to extend dynamically its knowledge about the world. We contribute to this challenge with a deep learning architecture that can dynamically update its known classes in an end-to-end fashion. The proposed deep network, based on a deep extension of a non-parametric model, detects whether a perceived object belongs to the set of categories known by the system and learns it without the need to retrain the whole system from scratch. Annotated images about the new category can be provided by an 'oracle' (i.e. human supervision), or by autonomous mining of the Web. Experiments on two different databases and on a robot platform demonstrate the promise of our approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
