PMT-MAE: Dual-Branch Self-Supervised Learning with Distillation for Efficient Point Cloud Classification
<|reference_start|>PMT-MAE: Dual-Branch Self-Supervised Learning with Distillation for Efficient Point Cloud Classification: Advances in self-supervised learning are essential for enhancing feature extraction and understanding in point cloud processing. This paper introduces PMT-MAE (Point MLP-Transformer Masked Autoencoder), a novel self-supervised learning framework for point cloud classification. PMT-MAE features a dual-branch architecture that integrates Transformer and MLP components to capture rich features. The Transformer branch leverages global self-attention for intricate feature interactions, while the parallel MLP branch processes tokens through shared fully connected layers, offering a complementary feature transformation pathway. A fusion mechanism then combines these features, enhancing the model's capacity to learn comprehensive 3D representations. Guided by the sophisticated teacher model Point-M2AE, PMT-MAE employs a distillation strategy that includes feature distillation during pre-training and logit distillation during fine-tuning, ensuring effective knowledge transfer. On the ModelNet40 classification task, achieving an accuracy of 93.6\% without employing voting strategy, PMT-MAE surpasses the baseline Point-MAE (93.2\%) and the teacher Point-M2AE (93.4\%), underscoring its ability to learn discriminative 3D point cloud representations. Additionally, this framework demonstrates high efficiency, requiring only 40 epochs for both pre-training and fine-tuning. PMT-MAE's effectiveness and efficiency render it well-suited for scenarios with limited computational resources, positioning it as a promising solution for practical point cloud analysis.<|reference_end|>
@article{zheng2024pmt-mae:,
    title={PMT-MAE: Dual-Branch Self-Supervised Learning with Distillation for
  Efficient Point Cloud Classification},
    author={Qiang Zheng, Chao Zhang, Jian Sun},
    journal={arXiv preprint arXiv:2409.02007},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.02007},
    primaryClass={cs.CV}
}