面向少样本类增量学习的主动类别选择
Active Class Selection for Few-Shot Class-Incremental Learning
AI总结：
本文提出FIASco框架，将少样本类增量学习与主动类别选择相结合，并集成势场导航，使机器人能自主选择并标注环境中最具信息量的对象进行持续学习，在模拟和真实机器人上验证了其有效性。
摘要：
对于现实世界应用，机器人需要通过与其用户的有限交互在其环境中持续学习。为此，先前在少样本类增量学习（FSCIL）和主动类别选择（ACS）方面的工作已取得有前景的结果，但均在受限设置下进行了测试。因此，在本文中，我们结合FSCIL和ACS的思想，开发了一个新颖框架，使自主智能体能够通过要求其用户仅标注环境中最具信息量的少数对象来持续学习新对象。为此，我们基于最先进的（SOTA）FSCIL模型，并利用ACS文献中的技术对其进行扩展。我们将该模型称为Few-shot Incremental Active class SeleCtiOn（FIASco）。我们进一步将基于势场的导航技术与我们的模型集成，开发了一个完整框架，使智能体能够通过FIASco模型处理并推理其传感数据，导航至环境中最具信息量的对象，通过其传感器收集关于该对象的数据，并增量更新FIASco模型。在模拟智能体和真实机器人上的实验结果表明了我们的方法对于长期现实世界机器人应用的重要性。
英文摘要：
For real-world applications, robots will need to continually learn in their environments through limited interactions with their users. Toward this, previous works in few-shot class incremental learning (FSCIL) and active class selection (ACS) have achieved promising results but were tested in constrained setups. Therefore, in this paper, we combine ideas from FSCIL and ACS to develop a novel framework that can allow an autonomous agent to continually learn new objects by asking its users to label only a few of the most informative objects in the environment. To this end, we build on a state-of-the-art (SOTA) FSCIL model and extend it with techniques from ACS literature. We term this model Few-shot Incremental Active class SeleCtiOn (FIASco). We further integrate a potential field-based navigation technique with our model to develop a complete framework that can allow an agent to process and reason on its sensory data through the FIASco model, navigate towards the most informative object in the environment, gather data about the object through its sensors and incrementally update the FIASco model. Experimental results on a simulated agent and a real robot show the significance of our approach for long-term real-world robotics applications.
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。
