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.
CITATION STYLE
Kissner, M., & Mayer, H. (2019). A Neural-Symbolic Architecture for Inverse Graphics Improved by Lifelong Meta-learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11824 LNCS, pp. 471–484). Springer. https://doi.org/10.1007/978-3-030-33676-9_33
Mendeley helps you to discover research relevant for your work.