PointABM:Integrating Bidirectional State Space Model with Multi-Head Self-Attention for Point Cloud Analysis
<|reference_start|>PointABM:Integrating Bidirectional State Space Model with Multi-Head Self-Attention for Point Cloud Analysis: Mamba, based on state space model (SSM) with its linear complexity and great success in classification provide its superiority in 3D point cloud analysis. Prior to that, Transformer has emerged as one of the most prominent and successful architectures for point cloud analysis. We present PointABM, a hybrid model that integrates the Mamba and Transformer architectures for enhancing local feature to improve performance of 3D point cloud analysis. In order to enhance the extraction of global features, we introduce a bidirectional SSM (bi-SSM) framework, which comprises both a traditional token forward SSM and an innovative backward SSM. To enhance the bi-SSM's capability of capturing more comprehensive features without disrupting the sequence relationships required by the bidirectional Mamba, we introduce Transformer, utilizing its self-attention mechanism to process point clouds. Extensive experimental results demonstrate that integrating Mamba with Transformer significantly enhance the model's capability to analysis 3D point cloud.<|reference_end|>
@article{chen2024pointabm:integrating,
    title={PointABM:Integrating Bidirectional State Space Model with Multi-Head
  Self-Attention for Point Cloud Analysis},
    author={Jia-wei Chen, Yu-jie Xiong, Yong-bin Gao},
    journal={arXiv preprint arXiv:2406.06069},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06069},
    primaryClass={cs.CV}
}