终身元学习改进的逆向图形神经符号架构
A Neural-Symbolic Architecture for Inverse Graphics Improved by Lifelong Meta-Learning
摘要：
我们遵循的想法，制定视觉作为逆图形，并提出了一种新的类型的元素，这项任务，一个神经象征胶囊。它能够将场景去渲染为语义信息前馈，以及渲染它的反馈。从生成语法中获得用于图形基元的胶囊的初始集合，并将其连接到完整的胶囊网络中。终身元学习通过为它使用少样本学习在场景中检测到的新的和更复杂的对象添加胶囊来不断提高该网络的检测能力。初步结果证明了我们新方法的潜力。
英文摘要：
We follow the idea of formulating vision as inverse graphics and propose a new type of element for this task, a neural-symbolic capsule. It is capable of de-rendering a scene into semantic information feed-forward, as well as rendering it feed-backward. An initial set of capsules for graphical primitives is obtained from a generative grammar and connected into a full capsule network. Lifelong meta-learning continuously improves this network's detection capabilities by adding capsules for new and more complex objects it detects in a scene using few-shot learning. Preliminary results demonstrate the potential of our novel approach.
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。
