Due to the emergence of powerful computing resources and large-scale
annotated datasets, deep learning has seen wide applications in our daily life.
However, most current methods require extensive data collection and retraining
when dealing with novel classes never seen before. On the other hand, we humans
can quickly recognize new classes by looking at a few samples, which motivates
the recent popularity of few-shot learning (FSL) in machine learning
communities. Most current FSL approaches work on 2D image domain, however, its
implication in 3D perception is relatively under-explored. Not only needs to
recognize the unseen examples as in 2D domain, 3D few-shot learning is more
challenging with unordered structures, high intra-class variances, and subtle
inter-class differences. Moreover, different architectures and learning
algorithms make it difficult to study the effectiveness of existing 2D methods
when migrating to the 3D domain. In this work, for the first time, we perform
systematic and extensive studies of recent 2D FSL and 3D backbone networks for
benchmarking few-shot point cloud classification, and we suggest a strong
baseline and learning architectures for 3D FSL. Then, we propose a novel
plug-and-play component called Cross-Instance Adaptation (CIA) module, to
address the high intra-class variances and subtle inter-class differences
issues, which can be easily inserted into current baselines with significant
performance improvement. Extensive experiments on two newly introduced
benchmark datasets, ModelNet40-FS and ShapeNet70-FS, demonstrate the
superiority of our proposed network for 3D FSL.

该研究对最近在机器学习领域受到欢迎的少样本学习在三维感知上的应用进行了深入系统研究，提出了一种新的组件 Cross-Instance Adaptation（CIA） module，将其插入到当前基线模型中可显著提高表现，并在两个新的基准数据集上证明了该模型的优越性。