Pointmamba: A simple state space model for point cloud analysis
Transformers have become one of the foundational architectures in point cloud analysis
tasks due to their excellent global modeling ability. However, the attention mechanism has …
tasks due to their excellent global modeling ability. However, the attention mechanism has …
Mamba in vision: A comprehensive survey of techniques and applications
Mamba is emerging as a novel approach to overcome the challenges faced by
Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) in computer vision …
Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) in computer vision …
Transformers to ssms: Distilling quadratic knowledge to subquadratic models
Transformer architectures have become a dominant paradigm for domains like language
modeling but suffer in many inference settings due to their quadratic-time self-attention …
modeling but suffer in many inference settings due to their quadratic-time self-attention …
Progressive Classifier and Feature Extractor Adaptation for Unsupervised Domain Adaptation on Point Clouds
Unsupervised domain adaptation (UDA) is a critical challenge in the field of point cloud
analysis. Previous works tackle the problem either by feature extractor adaptation to enable …
analysis. Previous works tackle the problem either by feature extractor adaptation to enable …
Voxel Mamba: Group-Free State Space Models for Point Cloud based 3D Object Detection
Serialization-based methods, which serialize the 3D voxels and group them into multiple
sequences before inputting to Transformers, have demonstrated their effectiveness in 3D …
sequences before inputting to Transformers, have demonstrated their effectiveness in 3D …
GraspMamba: A Mamba-based Language-driven Grasp Detection Framework with Hierarchical Feature Learning
Grasp detection is a fundamental robotic task critical to the success of many industrial
applications. However, current language-driven models for this task often struggle with …
applications. However, current language-driven models for this task often struggle with …
MambaPlace: Text-to-Point-Cloud Cross-Modal Place Recognition with Attention Mamba Mechanisms
T Shang, Z Li, W Pei, P Xu, ZJ Deng, F Kong - arXiv preprint arXiv …, 2024 - arxiv.org
Vision Language Place Recognition (VLVPR) enhances robot localization performance by
incorporating natural language descriptions from images. By utilizing language information …
incorporating natural language descriptions from images. By utilizing language information …
Towards 3D Semantic Scene Completion for Autonomous Driving: A Meta-Learning Framework Empowered by Deformable Large-Kernel Attention and Mamba Model
Semantic scene completion (SSC) is essential for achieving comprehensive perception in
autonomous driving systems. However, existing SSC methods often overlook the high …
autonomous driving systems. However, existing SSC methods often overlook the high …
DSDFormer: An Innovative Transformer-Mamba Framework for Robust High-Precision Driver Distraction Identification
J Chen, Z Zhang, J Yu, H Huang, R Zhang, X Xu… - arXiv preprint arXiv …, 2024 - arxiv.org
Driver distraction remains a leading cause of traffic accidents, posing a critical threat to road
safety globally. As intelligent transportation systems evolve, accurate and real-time …
safety globally. As intelligent transportation systems evolve, accurate and real-time …
MambaOcc: Visual State Space Model for BEV-based Occupancy Prediction with Local Adaptive Reordering
Occupancy prediction has attracted intensive attention and shown great superiority in the
development of autonomous driving systems. The fine-grained environmental …
development of autonomous driving systems. The fine-grained environmental …