工作记忆辅助下的多模态自我监督人识别认知结构
Cognitive architecture aided by working-memory for self-supervised multi-modal humans recognition
摘要：
识别人类伙伴的能力是建立个性化和长期人机交互的重要社交技能，特别是在教育，护理和康复等场景中。面孔和声音是两个重要的信息来源，使人工系统能够可靠地识别个人。深度学习网络已经取得了最先进的成果，并被证明是解决这一任务的合适工具。然而，当这些网络被应用于训练集中没有包括的不同和前所未有的场景时，它们的性能可能会下降。例如，在不断变化和现实的环境中，机器人平台总是获得新的感官证据，这些模型的性能会下降。一种解决方案是让机器人通过自我监督从第一手感官数据中学习。这允许处理在现实和交互式环境中收集的数据的固有可变性。为此，我们提出了一个认知架构整合低层次的知觉过程与空间工作记忆机制。该架构自主地将机器人的感官体验组织成适合人类识别的结构化数据集。我们的研究结果表明，我们的架构的有效性，并表明，这是一个很有前途的解决方案，在寻求使机器人在他们的学习过程中更加自主。
英文摘要：
The ability to recognize human partners is an important social skill to build personalized and long-term human-robot interactions, especially in scenarios like education, care-giving, and rehabilitation. Faces and voices constitute two important sources of information to enable artificial systems to reliably recognize individuals. Deep learning networks have achieved state-of-the-art results and demonstrated to be suitable tools to address such a task. However, when those networks are applied to different and unprecedented scenarios not included in the training set, they can suffer a drop in performance. For example, with robotic platforms in ever-changing and realistic environments, where always new sensory evidence is acquired, the performance of those models degrades. One solution is to make robots learn from their first-hand sensory data with self-supervision. This allows coping with the inherent variability of the data gathered in realistic and interactive contexts. To this aim, we propose a cognitive architecture integrating low-level perceptual processes with a spatial working memory mechanism. The architecture autonomously organizes the robot's sensory experience into a structured dataset suitable for human recognition. Our results demonstrate the effectiveness of our architecture and show that it is a promising solution in the quest of making robots more autonomous in their learning process.
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。
