Results 161 to 170 of about 13,981 (216)

Demystify Mamba in Vision: A Linear Attention Perspective

Neural Information Processing Systems
Mamba is an effective state space model with linear computation complexity. It has recently shown impressive efficiency in dealing with high-resolution inputs across various vision tasks.
Dongchen Han, Ziyi Wang, Zhuofan Xia, Yizeng Han, Yifan Pu, Chunjiang Ge, Jun Song, Shiji Song, Bo Zheng, Gao Huang   +9 more
semanticscholar   +1 more source

E-Mamba: Efficient Mamba network for hyperspectral and LiDAR joint classification

Information Fusion
Zhonghao Chen, Hongmin Gao, Pedram Ghamisi   +2 more
exaly   +2 more sources

MambaOut: Do We Really Need Mamba for Vision?*

Computer Vision and Pattern Recognition
Mamba, an architecture with RNN-like token mixer of state space model (SSM), was recently introduced to address the quadratic complexity of the attention mechanism and subsequently applied to vision tasks.
Weihao Yu, Xinchao Wang
semanticscholar   +1 more source

S³-Mamba: Small-Size-Sensitive Mamba for Lesion Segmentation

Proceedings of the AAAI Conference on Artificial Intelligence
Small lesions play a critical role in early disease diagnosis and intervention of severe infections. Popular models often face challenges in segmenting small lesions, as it occupies only a minor portion of an image, while down-sampling operations may inevitably lose focus on local features of small lesions.
Gui Wang, Yuexiang Li, Wenting Chen, Meidan Ding, Wooi Ping Cheah, Rong Qu, Jianfeng Ren, Linlin Shen   +7 more
openaire   +1 more source

H-vmunet: High-order Vision Mamba UNet for Medical Image Segmentation

Neurocomputing
In the field of medical image segmentation, variant models based on Convolutional Neural Networks (CNNs) and Visual Transformers (ViTs) as the base modules have been very widely developed and applied.
Renkai Wu, Yinghao Liu, Pengchen Liang, Qing Chang   +3 more
semanticscholar   +1 more source

CDMamba: Incorporating Local Clues Into Mamba for Remote Sensing Image Binary Change Detection

IEEE Transactions on Geoscience and Remote Sensing
Recently, the Mamba architecture based on state-space models has demonstrated remarkable performance in a series of natural language processing tasks and has been rapidly applied to remote sensing change detection (CD) tasks.
Haotian Zhang, Keyan Chen, Chenyang Liu, Hao Chen, Zhengxia Zou, Z. Shi   +5 more
semanticscholar   +1 more source

Pan-Mamba: Effective pan-sharpening with State Space Model

Information Fusion
Pan-sharpening involves integrating information from low-resolution multi-spectral and high-resolution panchromatic images to generate high-resolution multi-spectral counterparts.
Xuanhua He, Ke Cao, K. Yan, Rui Li, Chengjun Xie, Jie Zhang, Man Zhou   +6 more
semanticscholar   +1 more source

PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition

British Machine Vision Conference
We present PlainMamba: a simple non-hierarchical state space model (SSM) designed for general visual recognition. The recent Mamba model has shown how SSMs can be highly competitive with other architectures on sequential data and initial attempts have ...
Chenhongyi Yang, Zehui Chen, Miguel Espinosa, Linus Ericsson, Zhenyu Wang, Jiaming Liu, Elliot Crowley   +6 more
semanticscholar   +1 more source

Vision Mamba: A Comprehensive Survey and Taxonomy

arXiv.org
State Space Model (SSM) is a mathematical model used to describe and analyze the behavior of dynamic systems. This model has witnessed numerous applications in several fields, including control theory, signal processing, economics and machine learning ...
Xiao Liu, Chenxu Zhang, Lei Zhang
semanticscholar   +1 more source

LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation

arXiv.org
UNet and its variants have been widely used in medical image segmentation. However, these models, especially those based on Transformer architectures, pose challenges due to their large number of parameters and computational loads, making them unsuitable
Weibin Liao, Yinghao Zhu, Xinyuan Wang, Cehngwei Pan, Yasha Wang, Liantao Ma   +5 more
semanticscholar   +1 more source