Results 91 to 100 of about 6,939 (213)

Mamba meets crack segmentation

Cracks pose safety risks to infrastructure and cannot be overlooked. The prevailing structures in existing crack segmentation networks predominantly consist of CNNs or Transformers.
Wang, Yu-Hsing, He, Zhili
core  

Breast Ultrasound Image Segmentation Integrating Mamba-CNN and Feature Interaction

Sensors
The large scale and shape variation in breast lesions make their segmentation extremely challenging. A breast ultrasound image segmentation model integrating Mamba-CNN and feature interaction is proposed for breast ultrasound images with a large amount ...
Guoliang Yang, Yuyu Zhang, Hao Yang
doaj   +1 more source

{S$^3$-Mamba}: Small-Size-Sensitive Mamba for Lesion Segmentation

CoRR
Accept by AAAI ...
Gui Wang, Yuexiang Li, Wenting Chen, Meidan Ding, Wooi Ping Cheah, Rong Qu, Jianfeng Ren, Linlin Shen   +7 more
openaire   +2 more sources

Mamba-UNet:UNet-Like Pure Visual Mamba for Medical Image Segmentation

In recent advancements in medical image analysis, Convolutional Neural Networks (CNN) and Vision Transformers (ViT) have set significant benchmarks.
Zhang, Yichi, Wang, Ziyang, Zheng, Jian-Qing, Cui, Ge, Li, Lei   +4 more
core   +1 more source

Dual-Mamba-ResNet: A Novel Vision State Space Network for Aero-Engine Ablation Detection

Aerospace
With the rapid development of the aviation industry, engines operate under extreme conditions of high temperature, high pressure, and high vibration, making them prone to surface damage such as ablation. Ablation not only affects the structural integrity
Xin Wang, Hai Shu, Yaxi Xu, Qiang Fu, Jide Qian   +4 more
doaj   +1 more source

Deep Mamba Multi-modal Learning

Inspired by the excellent performance of Mamba networks, we propose a novel Deep Mamba Multi-modal Learning (DMML). It can be used to achieve the fusion of multi-modal features. We apply DMML to the field of multimedia retrieval and propose an innovative
Lyu, Bo, Huang, Zhangmin, Cui, Yu, Zou, Xin, Hu, Chenshu, Zhu, Jian   +5 more
core  

Integrating CNN and Mamba for Effective Time Series Forecasting

Journal of Applied Science and Engineering
Recently, Transformer-based models have achieved remarkable progress in time series forecasting, yet they still suffer from limitations in efficiency and scalability due to quadratic complexity.
Junliang Tao, Hongbing Wang, Li Cao, Chenhao Xie, CJian Li, Liang Zhou   +5 more
doaj   +1 more source

ML-Mamba: Efficient Multi-Modal Large Language Model Utilizing Mamba-2

Multimodal Large Language Models (MLLMs) have attracted much attention for their multifunctionality. However, traditional Transformer architectures incur significant overhead due to their secondary computational complexity.
Ding, Yanyu, Wang, Zhengzhuo, Hu, Jianguo, Pan, Jiakai, Huang, Wenjun, Wang, Yuhe, Tang, Jiahao, Xing, Yifei   +7 more
core  

Q-Mamba: On First Exploration of Vision Mamba for Image Quality Assessment

In this work, we take the first exploration of the recently popular foundation model, i.e., State Space Model/Mamba, in image quality assessment, aiming at observing and excavating the perception potential in vision Mamba.
Yu, Zihao, Guan, Fengbin, Chen, Zhibo, Lu, Yiting, Li, Xin   +4 more
core  

3DSS-Mamba: 3D-Spectral-Spatial Mamba for Hyperspectral Image Classification

Hyperspectral image (HSI) classification constitutes the fundamental research in remote sensing fields. Convolutional Neural Networks (CNNs) and Transformers have demonstrated impressive capability in capturing spectral-spatial contextual dependencies ...
Li, Jun, Liu, Bo, He, Yan, Tu, Bing, Plaza, Antonio   +4 more
core