论文标题
域不变的暹罗注意面膜，用于通过日常室内机器人导航进行小物体变化检测
Domain Invariant Siamese Attention Mask for Small Object Change Detection via Everyday Indoor Robot Navigation
论文作者
论文摘要
通过每天的室内机器人导航的图像变化检测问题是从自我发项技术的新角度探索的。在机器人社区中，检测语义上非敏感性和视觉上很小的变化仍然是一个关键挑战。从直觉上讲，这些小小的不敏感性变化可以通过注意机制的最新范式来更好地处理这项工作的基本思想。但是，现有的自我发挥模型需要每个域的重大再培训成本，因此它不直接适用于机器人应用程序。我们提出了一种新的自我发场技术，具有无监督的直立域适应能力，该技术将注意力掩码引入了图像变化检测模型的中间层，而无需修改模型的输入和输出层。室内机器人旨在检测日常导航的视觉变化的实验表明，我们的注意力技术显着增强了最新的图像变化检测模型。
The problem of image change detection via everyday indoor robot navigation is explored from a novel perspective of the self-attention technique. Detecting semantically non-distinctive and visually small changes remains a key challenge in the robotics community. Intuitively, these small non-distinctive changes may be better handled by the recent paradigm of the attention mechanism, which is the basic idea of this work. However, existing self-attention models require significant retraining cost per domain, so it is not directly applicable to robotics applications. We propose a new self-attention technique with an ability of unsupervised on-the-fly domain adaptation, which introduces an attention mask into the intermediate layer of an image change detection model, without modifying the input and output layers of the model. Experiments, in which an indoor robot aims to detect visually small changes in everyday navigation, demonstrate that our attention technique significantly boosts the state-of-the-art image change detection model.
