Robotic visual systems operating in the wild must act in unconstrained scenarios, under different environmental conditions while facing a variety of semantic concepts, including unknown ones. To this end, recent works tried to empower visual object recognition methods with the capability to i) detect unseen concepts and ii) extended their knowledge over time, as images of new semantic classes arrive. This setting, called Open World Recognition (OWR), has the goal to produce systems capable of breaking the semantic limits present in the initial training set. However, this training set imposes to the system not only its own semantic limits, but also environmental ones, due to its bias toward certain acquisition conditions that do not necessarily reflect the high variability of the real-world. This discrepancy between training and test distribution is called domain-shift. This letter investigates whether OWR algorithms are effective under domain-shift, presenting the first benchmark setup for assessing fairly the performances of OWR algorithms, with and without domain-shift. We then use this benchmark to conduct analyses in various scenarios, showing how existing OWR algorithms indeed suffer a severe performance degradation when train and test distributions differ. Our analysis shows that this degradation is only slightly mitigated by coupling OWR with domain generalization techniques, indicating that the mere plug-and-play of existing algorithms is not enough to recognize new and unknown categories in unseen domains. Our results clearly point toward open issues and future research directions, that need to be investigated for building robot visual systems able to function reliably under these challenging yet very real conditions.

中文翻译：

---

论视域转移下开放世界识别的挑战

在野外运行的机器人视觉系统必须在不受约束的场景中行动，在不同的环境条件下，同时面对各种语义概念，包括未知的概念。为此，最近的工作试图使视觉对象识别方法具有以下能力：i) 检测看不见的概念，以及 ii) 随着新语义类图像的到来，随着时间的推移扩展他们的知识。此设置称为开放世界识别 (OWR)，其目标是生成能够打破初始训练集中存在的语义限制的系统。然而，该训练集不仅对系统施加了其自身的语义限制，而且还对系统施加了环境限制，因为它偏向于某些不一定反映现实世界高度可变性的获取条件。训练和测试分布之间的这种差异称为域转移。这封信调查了 OWR 算法在域转移下是否有效，提出了第一个基准设置，用于公平评估 OWR 算法的性能，无论有没有域转移。然后我们使用这个基准在各种场景中进行分析，展示现有的 OWR 算法在训练和测试分布不同时确实会遭受严重的性能下降。我们的分析表明，通过将 OWR 与域泛化技术相结合，这种退化只能略微减轻，这表明现有算法的即插即用不足以识别未知域中的新类别和未知类别。我们的结果清楚地指向了未解决的问题和未来的研究方向，