论文标题
AI在线过滤到现实世界图像识别
AI Online Filters to Real World Image Recognition
论文作者
论文摘要
当今，在许多视觉和机器人应用中广泛使用了经过标记的数据集训练的深人造神经网络。就AI而言，这些被称为反射模型，指的是它们不自我发展或积极适应环境变化的事实。随着对智能机器人控制的需求扩大到许多高级任务，强化学习和基于州的模型起着越来越重要的作用。在此，在计算机视觉和机器人域中，我们研究了一种新的方法，可以在图像识别反射模型中添加强化控件，以实现更好的整体性能，特别是在更广泛的环境范围内，超出了任务反射模型的期望。遵循具有环境感应和基于AI的自适应剂建模的常见基础架构，我们实施了多种类型的AI控制剂。最后，我们提供了这些代理商的比较结果，并对它们的利益进行了深入的分析，以提高现实世界中的整体图像识别性能。
Deep artificial neural networks, trained with labeled data sets are widely used in numerous vision and robotics applications today. In terms of AI, these are called reflex models, referring to the fact that they do not self-evolve or actively adapt to environmental changes. As demand for intelligent robot control expands to many high level tasks, reinforcement learning and state based models play an increasingly important role. Herein, in computer vision and robotics domain, we study a novel approach to add reinforcement controls onto the image recognition reflex models to attain better overall performance, specifically to a wider environment range beyond what is expected of the task reflex models. Follow a common infrastructure with environment sensing and AI based modeling of self-adaptive agents, we implement multiple types of AI control agents. To the end, we provide comparative results of these agents with baseline, and an insightful analysis of their benefit to improve overall image recognition performance in real world.
