No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentation
<|reference_start|>No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentation: To reduce the reliance on large-scale datasets, recent works in 3D segmentation resort to few-shot learning. Current 3D few-shot segmentation methods first pre-train models on 'seen' classes, and then evaluate their generalization performance on 'unseen' classes. However, the prior pre-training stage not only introduces excessive time overhead but also incurs a significant domain gap on 'unseen' classes. To tackle these issues, we propose a Non-parametric Network for few-shot 3D Segmentation, Seg-NN, and its Parametric variant, Seg-PN. Without training, Seg-NN extracts dense representations by hand-crafted filters and achieves comparable performance to existing parametric models. Due to the elimination of pre-training, Seg-NN can alleviate the domain gap issue and save a substantial amount of time. Based on Seg-NN, Seg-PN only requires training a lightweight QUEry-Support Transferring (QUEST) module, which enhances the interaction between the support set and query set. Experiments suggest that Seg-PN outperforms previous state-of-the-art method by +4.19% and +7.71% mIoU on S3DIS and ScanNet datasets respectively, while reducing training time by -90%, indicating its effectiveness and efficiency.<|reference_end|>
@article{zhu2024no,
    title={No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D
  Scene Segmentation},
    author={Xiangyang Zhu, Renrui Zhang, Bowei He, Ziyu Guo, Jiaming Liu, Han
  Xiao, Chaoyou Fu, Hao Dong, Peng Gao},
    journal={arXiv preprint arXiv:2404.04050},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.04050},
    primaryClass={cs.CV}
}