SimpliMix: A Simplified Manifold Mixup for Few-shot Point Cloud Classification

Few-shot learning often assumes that base classes are abundant and diverse with plentiful well-labeled samples for each class. This ensures that models can generalize effectively from a small amount of data by leveraging prior knowledge learned from base classes. This assumption holds for 2D few-shot learning since the benchmark datasets are large and diverse. However, 3D point cloud few-shot benchmarks are low in magnitude and diversity. We conduct experiments and show that many existing methods overlook this issue and suffer from overfitting on base classes, which hinders generalization ability and test performance. To alleviate the overfitting issue, we propose a simplified manifold mixup, referred to as the SimpliMix, which mixes hidden representations and forces the models to learn more generalized features. We incorporate SimpliMix into existing prototype-based models, perform experiments on ModelNet40-FS, ModelNet40-C-FS and ScanObjectNN-FS datasets, and improve the models by a significant margin. We further conduct cross-domain few-shot classification experiments and show that networks with SimpliMix learn more generalized and transferable features and achieve better performance. The code is available at https://github.com/LexieYang/SimpliMix