PoinTramba: A Hybrid Transformer-Mamba Framework for Point Cloud Analysis

• URL: http://arxiv.org/abs/2405.15463v2
• Date: Sun, 16 Jun 2024 15:07:23 GMT
• Title: PoinTramba: A Hybrid Transformer-Mamba Framework for Point Cloud Analysis
• Authors: Zicheng Wang, Zhenghao Chen, Yiming Wu, Zhen Zhao, Luping Zhou, Dong Xu,
• Abstract summary: PoinTramba is a hybrid framework that combines the analytical power of Transformer with the remarkable computational efficiency of Mamba. Our approach first segments point clouds into groups, where the Transformer meticulously captures intricate intra-group dependencies. Unlike previous Mamba approaches, we introduce a bi-directional importance-aware ordering (BIO) strategy to tackle the challenges of random ordering effects.
• Score: 37.18701051669003
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point cloud analysis has seen substantial advancements due to deep learning, although previous Transformer-based methods excel at modeling long-range dependencies on this task, their computational demands are substantial. Conversely, the Mamba offers greater efficiency but shows limited potential compared with Transformer-based methods. In this study, we introduce PoinTramba, a pioneering hybrid framework that synergies the analytical power of Transformer with the remarkable computational efficiency of Mamba for enhanced point cloud analysis. Specifically, our approach first segments point clouds into groups, where the Transformer meticulously captures intricate intra-group dependencies and produces group embeddings, whose inter-group relationships will be simultaneously and adeptly captured by efficient Mamba architecture, ensuring comprehensive analysis. Unlike previous Mamba approaches, we introduce a bi-directional importance-aware ordering (BIO) strategy to tackle the challenges of random ordering effects. This innovative strategy intelligently reorders group embeddings based on their calculated importance scores, significantly enhancing Mamba's performance and optimizing the overall analytical process. Our framework achieves a superior balance between computational efficiency and analytical performance by seamlessly integrating these advanced techniques, marking a substantial leap forward in point cloud analysis. Extensive experiments on datasets such as ScanObjectNN, ModelNet40, and ShapeNetPart demonstrate the effectiveness of our approach, establishing a new state-of-the-art analysis benchmark on point cloud recognition. For the first time, this paradigm leverages the combined strengths of both Transformer and Mamba architectures, facilitating a new standard in the field. The code is available at https://github.com/xiaoyao3302/PoinTramba.

Related papers

• From Markov to Laplace: How Mamba In-Context Learns Markov Chains [36.22373318908893]
  We study in-context learning on Markov chains and uncover a surprising phenomenon. Unlike transformers, even a single-layer Mamba efficiently learns the in-context Laplacian smoothing estimator. These theoretical insights align strongly with empirical results and represent the first formal connection between Mamba and optimal statistical estimators.
  arXiv  Detail & Related papers  (2025-02-14T14:13:55Z)
• A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers [0.29687381456164]
  This study presents a comparative analysis of dynamic graph embedding approaches using transformers and the recently proposed Mamba architecture. We introduce three novel models: TransformerG2G augment with graph convolutional networks, DG-Mamba, and GDG-Mamba with graph isomorphism network edge convolutions. Our experiments on multiple benchmark datasets demonstrate that Mamba-based models achieve comparable or superior performance to transformer-based approaches in link prediction tasks.
  arXiv  Detail & Related papers  (2024-12-15T19:56:56Z)
• Exploring contextual modeling with linear complexity for point cloud segmentation [43.36716250540622]
  We identify the key components of an effective and efficient point cloud segmentation architecture. We show that Mamba features linear computational complexity, offering superior data and inference efficiency compared to Transformers. We further enhance the standard Mamba specifically for point cloud segmentation by identifying its two key shortcomings.
  arXiv  Detail & Related papers  (2024-10-28T16:56:30Z)
• ReMamba: Equip Mamba with Effective Long-Sequence Modeling [50.530839868893786]
  We propose ReMamba, which enhances Mamba's ability to comprehend long contexts. ReMamba incorporates selective compression and adaptation techniques within a two-stage re-forward process.
  arXiv  Detail & Related papers  (2024-08-28T02:47:27Z)
• SIGMA: Selective Gated Mamba for Sequential Recommendation [56.85338055215429]
  Mamba, a recent advancement, has exhibited exceptional performance in time series prediction. We introduce a new framework named Selective Gated Mamba ( SIGMA) for Sequential Recommendation. Our results indicate that SIGMA outperforms current models on five real-world datasets.
  arXiv  Detail & Related papers  (2024-08-21T09:12:59Z)
• Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis [63.66763657191476]
  We show that efficient numerical training and inference algorithms as low-rank computation have impressive performance for learning Transformer-based adaption. We analyze how magnitude-based models affect generalization while improving adaption. We conclude that proper magnitude-based has a slight on the testing performance.
  arXiv  Detail & Related papers  (2024-06-24T23:00:58Z)
• PointABM:Integrating Bidirectional State Space Model with Multi-Head Self-Attention for Point Cloud Analysis [8.500020888201231]
  Mamba, based on state space model (SSM) with its linear complexity and great success in classification provide its superiority in 3D point cloud analysis. 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.
  arXiv  Detail & Related papers  (2024-06-10T07:24:22Z)
• PointMamba: A Simple State Space Model for Point Cloud Analysis [65.59944745840866]
  We propose PointMamba, transferring the success of Mamba, a recent representative state space model (SSM), from NLP to point cloud analysis tasks. Unlike traditional Transformers, PointMamba employs a linear complexity algorithm, presenting global modeling capacity while significantly reducing computational costs.
  arXiv  Detail & Related papers  (2024-02-16T14:56:13Z)
• End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
  This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures. We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
  arXiv  Detail & Related papers  (2023-05-25T10:58:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.