Multimodal 3D Few‐Shot Classification via Gaussian Mixture Discriminant Analysis
Yiqi Wu, Huachao Wu, Ronglei Hu, Yilin Chen, Dejun Zhang · Computer Graphics Forum · 2025
Abstract While pre‐trained 3D vision‐language models are becoming increasingly available, there remains a lack of frameworks that can effectively harness their capabilities for few‐shot classification. In this work, we propose PointGMDA, a training‐free framework that combines Gaussian Mixture Models (GMMs) with Gaussian Discriminant Analysis (GDA) to perform robust classification using only a few labeled point cloud samples. Our method estimates GMM parameters per class from support data and computes mixture‐weighted prototypes, which are then used in GDA with a shared covariance matrix to construct decision boundaries. This formulation allows us to model intra‐class variability more expressively than traditional single‐prototype approaches, while maintaining analytical tractability. To incorporate semantic priors, we integrate CLIP‐style textual prompts and fuse predictions from geometric and textual modalities through a hybrid scoring strategy. We further introduce PointGMDA‐T, a lightweight attention‐guided refinement module that learns residuals for fast feature adaptation, improving robustness under distribution shift. Extensive experiments on ModelNet40 and ScanObjectNN demonstrate that PointGMDA outperforms strong baselines across a variety of few‐shot settings, with consistent gains under both training‐free and fine‐tuned conditions. These results highlight the effectiveness and generality of our probabilistic modeling and multimodal adaptation framework. Our code is publicly available at https://github.com/djzgroup/PointGMDA .