论文标题
自我监督的转移学习，例如通过物理互动细分
Self-supervised Transfer Learning for Instance Segmentation through Physical Interaction
论文作者
论文摘要
从图像中对未知对象进行实例分割被认为与几种机器人技能有关，包括抓握，跟踪和对象分类。计算机视觉的最新结果表明，大型手工标记的数据集可实现高分割性能。为了克服新环境中手动标记数据的耗时的过程，我们为机器人提供了一种转移学习方法，该方法通过以自我监督的方式与环境进行交互来学习细分对象。我们的机器人将未知的对象推在表上，并使用光流中的信息以对象蒙版的形式创建训练标签。为了实现这一目标，我们将现有的DeepMask网络微调，例如，对机器人获取的自标记的培训数据进行细分。我们在一组真实的图像上评估了训练有素的网络（SelfDeepMask），显示了具有新颖的对象的具有挑战性和混乱的场景。在这里，SelfDeepMask优于在可可数据集上训练的DeepMask网络的平均精度为9.5％。此外，我们将方法与最近的训练方法与嘈杂标签相结合，以更好地应对诱导的标签噪声。
Instance segmentation of unknown objects from images is regarded as relevant for several robot skills including grasping, tracking and object sorting. Recent results in computer vision have shown that large hand-labeled datasets enable high segmentation performance. To overcome the time-consuming process of manually labeling data for new environments, we present a transfer learning approach for robots that learn to segment objects by interacting with their environment in a self-supervised manner. Our robot pushes unknown objects on a table and uses information from optical flow to create training labels in the form of object masks. To achieve this, we fine-tune an existing DeepMask network for instance segmentation on the self-labeled training data acquired by the robot. We evaluate our trained network (SelfDeepMask) on a set of real images showing challenging and cluttered scenes with novel objects. Here, SelfDeepMask outperforms the DeepMask network trained on the COCO dataset by 9.5% in average precision. Furthermore, we combine our approach with recent approaches for training with noisy labels in order to better cope with induced label noise.
