Object detection plays a deep role in visual systems by identifying instances for downstream algorithms. In industrial scenarios, however, a slight change in manufacturing systems would lead to costly data re-collection and human annotation processes to re-train models. Existing solutions such as semi-supervised and few-shot methods either rely on numerous human annotations or suffer low performance. In this work, we explore a novel object detector based on interactive perception (ODIP), which can be adapted to novel domains in an automated manner. By interacting with a grasping system, ODIP accumulates visual observations of novel objects, learning to identify previously unseen instances without human-annotated data. Extensive experiments show ODIP outperforms both the generic object detector and state-of-the-art few-shot object detector fine-tuned in traditional manners. A demo video is provided to further illustrate the idea.

中文翻译：

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ODIP：通过交互式感知实现目标检测的自动适应

通过识别下游算法的实例，对象检测在视觉系统中发挥着重要作用。然而，在工业场景中，制造系统的轻微变化将导致代价高昂的数据重新收集和人工注释过程以重新训练模型。现有的解决方案，例如半监督和少样本方法，要么依赖大量人工注释，要么性能低下。在这项工作中，我们探索了一种基于交互式感知 (ODIP) 的新型对象检测器，它可以以自动化的方式适应新的领域。通过与抓取系统交互，ODIP 积累了对新物体的视觉观察，学习识别以前看不见的实例，而无需人工注释数据。大量实验表明，ODIP 的性能优于以传统方式微调的通用对象检测器和最先进的少镜头对象检测器。提供了一个演示视频来进一步说明这个想法。