A Neural-Symbolic Architecture for Inverse Graphics Improved by Lifelong Meta-Learning
arXiv:1905.08910 · doi:10.1007/978-3-030-33676-9
Abstract
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.
German Conference on Pattern Recognition (GCPR) 2019
References in corpus (9)
- Methods for Interpreting and Understanding Deep Neural Networks
- Deep Convolutional Inverse Graphics Network
- Interaction Networks for Learning about Objects, Relations and Physics
- Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding
- Non-Stationary Texture Synthesis by Adversarial Expansion
- The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
- Group Equivariant Capsule Networks
- Learning to Infer and Execute 3D Shape Programs
- 3D-Aware Scene Manipulation via Inverse Graphics