基于自顶向下和自底向上的信息交互的机器人视觉注意力引导模型
Guided Visual Attention Model Based on Interactions Between Top-down and Bottom-up Information for Robot Pose Prediction
摘要：
深度机器人视觉模型被广泛用于从相机图像中识别物体，但在未经训练的位置检测物体时表现不佳。虽然这个问题可以通过使用大数据集进行训练来缓解，但数据集收集成本不容忽视。现有的视觉注意力模型通过采用数据有效的结构来解决这个问题，该结构学习提取任务相关的图像区域。然而，由于模型不能在训练后修改注意目标，因此很难应用于动态变化的任务。提出了一种新的基于Key-Query-Value的视觉注意模型。该模型能够通过外部修改查询表示来切换注意目标，即自上而下的注意。该模型在模拟器和真实环境中进行了实验。在仿真实验中，该模型与现有的端到端机器人视觉模型进行了比较，显示出更高的性能和数据效率。在实际的机器人实验中，该模型显示了较高的精度以及可扩展性和可扩展性。
英文摘要：
Deep robot vision models are widely used for recognizing objects from camera images, but shows poor performance when detecting objects at untrained positions. Although such problem can be alleviated by training with large datasets, the dataset collection cost cannot be ignored. Existing visual attention models tackled the problem by employing a data efficient structure which learns to extract task relevant image areas. However, since the models cannot modify attention targets after training, it is difficult to apply to dynamically changing tasks. This paper proposed a novel Key-Query-Value formulated visual attention model. This model is capable of switching attention targets by externally modifying the Query representations, namely top-down attention. The proposed model is experimented on a simulator and a real-world environment. The model was compared to existing end-to-end robot vision models in the simulator experiments, showing higher performance and data efficiency. In the real-world robot experiments, the model showed high precision along with its scalability and extendibility.
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。
