Topology-Aware Graph Convolution Network for Few-Shot Incremental 3-D Object Learning

Three-dimensional (3-D) object recognition has achieved satisfied achievement in both academia and industry. However, most traditional 3-D object classification methods implicitly assume that there are abundant training data from a static distribution. To relax the assumption, we target on a more challenging and realistic setting: few-shot incremental 3-D object learning (FSI3DL), which intends to incrementally classify the new coming 3-D objects with few training data. In order to achieve this, two key challenges need to be concerned: 1) the catastrophic forgetting issue caused by incremental 3-D data with irregular and redundant topological structures and 2) the overfitting issue caused by few-shot training data. To address the first challenge, we use Laplacian spectral analysis based on 3-D meshes to design an embedding network that consists of super-vertex graph convolution (SVGC) module and topology-aware graph attention (TAGA) module. The SVGC is designed to construct the discriminative local topological characteristics for representing the irregular 3-D meshes better. The TAGA is designed to identify redundant topological characteristics. To address the second challenge, a fine-tuning strategy with model alignment regularization is investigated. Furthermore, an embedding space selection and fusion (ESSF) strategy is proposed in the inference phase to mitigate catastrophic forgetting and overfitting further. Combining SVGC, TAGA, and alignment regularization with ESSF strategy, a novel topology-aware graph convolution network (TopGCN) is proposed to address the FSI3DL. Experiments on representative 3-D classification datasets validate the superiority of TopGCN.