Multimodal 3D Few-Shot Classification via Gaussian Mixture Discriminant Analysis

Wu, Yiqi; Wu, Huachao; Hu, Ronglei; Chen, Yilin; Zhang, Dejun

Multimodal 3D Few-Shot Classification via Gaussian Mixture Discriminant Analysis

Files

cgf70268.pdf (4.52 MB)

Date

2025

Authors

Wu, Yiqi
Wu, Huachao
Hu, Ronglei
Chen, Yilin
Zhang, Dejun

Publisher

The Eurographics Association and John Wiley & Sons Ltd.

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 estimatesGMMparameters 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.

CCS Concepts: Computing methodologies → Computer graphics; 3D imaging; Computer vision; Machine learning

        @article{10.1111:cgf.70268
,
journal = {Computer Graphics Forum},
title = {{Multimodal 3D Few-Shot Classification via Gaussian Mixture Discriminant Analysis
}},
author = {Wu, Yiqi and 
Wu, Huachao and 
Hu, Ronglei and 
Chen, Yilin and 
Zhang, Dejun
},
year = {2025
},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.
},
ISSN = {1467-8659
},
DOI = {10.1111/cgf.70268
}
}