Results 141 to 150 of about 13,981 (216)

Study on mmage defect recognition and classification of power transmission equipment based on lightweight model residual Mamba. [PDF]

Sci Rep
Jindong C, Weijie S, Shan G, Liang S, Hongliang T, Fuwang W.   +5 more
europepmc   +1 more source

A Survey on Visual Mamba

Applied Sciences (Switzerland)
State space models (SSM) with selection mechanisms and hardware-aware architectures, namely Mamba, have recently shown significant potential in long-sequence modeling. Since the complexity of transformers’ self-attention mechanism is quadratic with image
Zi Ye, Ziyang Wang, Tianxiang Chen
exaly   +4 more sources

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Is Mamba effective for time series forecasting?

Neurocomputing
In the realm of time series forecasting (TSF), it is imperative for models to adeptly discern and distill hidden patterns within historical time series data to forecast future states. Transformer-based models exhibit formidable efficacy in TSF, primarily
Zihan Wang, Fanheng Kong, Shi Feng, Ming Wang, Han Zhao, Daling Wang, Yifei Zhang   +6 more
exaly   +2 more sources

Mamba-in-Mamba: Centralized Mamba-Cross-Scan in Tokenized Mamba Model for Hyperspectral image classification

Neurocomputing
Hyperspectral image (HSI) classification is pivotal in the remote sensing (RS) field, particularly with the advancement of deep learning techniques. Sequential models, adapted from the natural language processing (NLP) field such as Recurrent Neural Networks (RNNs) and Transformers, have been tailored to this task, offering a unique viewpoint. However,
Man Sing Wong, Haipeng Wang, Sei-Ichiro Kamata   +2 more
exaly   +3 more sources

Mamba-Stereo: Mamba Regularization for Stereo matching

Pattern Recognition
Jinxin Liu
exaly   +2 more sources

Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

International Conference on Machine Learning
Recently the state space models (SSMs) with efficient hardware-aware designs, i.e., the Mamba deep learning model, have shown great potential for long sequence modeling.
Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, Xinggang Wang   +5 more
semanticscholar   +1 more source

VM-UNet: Vision Mamba UNet for Medical Image Segmentation

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
In the realm of medical image segmentation, both CNN-based and Transformer-based models have been extensively explored. However, CNNs exhibit limitations in long-range modeling capabilities, whereas Transformers are hampered by their quadratic ...
Jiacheng Ruan, Jincheng Li, Suncheng Xiang   +2 more
semanticscholar   +1 more source

U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation

arXiv.org
Convolutional Neural Networks (CNNs) and Transformers have been the most popular architectures for biomedical image segmentation, but both of them have limited ability to handle long-range dependencies because of inherent locality or computational ...
Jun Ma, Feifei Li, Bo Wang
semanticscholar   +1 more source

MambaVision: A Hybrid Mamba-Transformer Vision Backbone

Computer Vision and Pattern Recognition
We propose a novel hybrid Mamba-Transformer backbone, MambaVision, specifically tailored for vision applications. Our core contribution includes redesigning the Mamba formulation to enhance its capability for efficient modeling of visual features ...
Ali Hatamizadeh, Jan Kautz
semanticscholar   +1 more source

NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model

arXiv.org
We introduce Nemotron-Nano-9B-v2, a hybrid Mamba-Transformer language model designed to increase throughput for reasoning workloads while achieving state-of-the-art accuracy compared to similarly-sized models. Nemotron-Nano-9B-v2 builds on the Nemotron-H
Nvidia Aarti Basant, Abhijit Khairnar, Abhijit Paithankar, Abhinav Khattar, Adi Renduchintala, Adi Renduchintala, A. Malte, A. Bercovich, Akshay Hazare, A. Rico, Aleksander Ficek, Alex Kondratenko, A. Shaposhnikov, Ali Taghibakhshi, Amelia Barton, A. Mahabaleshwarkar, Amy Shen, Andrew Tao, Ann Guan, Anna Shors, Anubhav Mandarwal, Arham Mehta, Arun Venkatesan, Ashton Sharabiani, Ashwath Aithal, Ashwin Poojary, Ayush Dattagupta, B. Buddharaju, Banghua Zhu, B. Simkin, Bilal Kartal, B. Rouhani, Bobby Chen, Boris Ginsburg, Brandon Norick, Brian Yu, Bryan Catanzaro, Charles Wang, C. Truong, Chetan Mungekar, Chintan Patel, Chris Alexiuk, Christian Munley, Christopher Parisien, Dan Su, Daniel Afrimi, Daniel Korzekwa, Daniel Rohrer, Daria Gitman, David Mosallanezhad, Deepak Narayanan, Dima Rekesh, Dina Yared, Dmytro Pykhtar, Dong Ahn, Duncan Riach, E. Long, Elliott Ning, Eric Chung, Erick Galinkin, E. Bakhturina, G. Prasad, Gerald Shen, Haim Elisha, Harsh Sharma, Hayley Ross, Helen Ngo, Herman Sahota, Hexin Wang, Hoo-Chang Shin, Hua Huang, Iain Cunningham, Igor Gitman, Ivan Moshkov, Jaehun Jung, Jan Kautz, Jane Scowcroft, J. Casper, Jimmy Zhang, Jinze Xue, Jocelyn Huang, Joey Conway, John Kamalu, J. Cohen, Joseph Jennings, J. V. Vialard, Junkeun Yi, Jupinder Parmar, Kari Briski, K. Cheung, K. Luna, Keith Wyss, Keshav Santhanam, Kezhi Kong, Krzysztof Pawelec, Kumar Anik, Kunlun Li, Kushan Ahmadian, Lawrence C. McAfee, Laya Sleiman, Leon Derczynski, Luis Vega, Maer Rodrigues de Melo, Makesh Narsimhan Sreedhar, Marcin Chochowski, Mark Cai, Markus Kliegl, Marta M. Stepniewska-Dziubinska, Matvei Novikov, Mehrzad Samadi, Meredith Price, Meriem Boubdir, Michael Boone, Michael Evans, Michał Bień, Michał Zawalski, Miguel Martínez, Mike Chrzanowski, M. Shoeybi, M. Patwary, N. Dhameja, Nave Assaf, N. Habibi, Nidhi Bhatia, Nikki Pope, Nima Tajbakhsh, Nirmal Juluru, O. Rybakov, Oleksii Hrinchuk, Oleksii Kuchaiev, O. Olabiyi, Pablo Ribalta, P. Subramanian, Parth Chadha, Pavlo Molchanov, Peter Dykas, Peter Jin, Piotr Bialecki, Piotr Januszewski, Pradeep Thalasta, P. Gaikwad, Prasoon Varshney, Pritam Gundecha, Przemek Tredak, Rabeeh Karimi Mahabadi, Rajendra J. Patel, Ran El-Yaniv, R. Rajan, Ria Cheruvu, Rima Shahbazyan, Ritika Borkar, Ritu Gala, R. Waleffe, Ruoxi Zhang, Russell J. Hewett, R. Prenger, Sahil Jain, Samuel Kriman, S. Satheesh, Saori Kaji, Sarah Yurick, Saurav Muralidharan, Sean Narenthiran, Seonmyeong Bak, Sepehr Sameni, Seungju Han, Shanmugam Ramasamy, Shaona Ghosh, Sharath Turuvekere Sreenivas, S. Thomas, Shizhe Diao, Shreya Gopal, Shrimai Prabhumoye, Shubham Toshniwal, Shuoyang Ding, Siddharth Singh, Siddhartha Jain, Somshubra Majumdar, Stefania Alborghetti, Syeda Nahida Akter, Terry Kong, Tim Moon, Tomasz Hliwiak, Tomer Asida, Tony Wang, Twinkle Vashishth, Tyler Poon, Udi Karpas, V. Noroozi, V. Srinivasan, V. Korthikanti, Vikram Fugro, Vineeth Kalluru, V. Kurin, Vitaly Lavrukhin, W. Ahmad, Wei Du, Wonmin Byeon, Ximing Lu, Xin Dong, Yashaswi Karnati, Yejin Choi, Yian Zhang, Ying Lin, Yonggan Fu, Yoshi Suhara, Zhen Dong, Zhiyu Li, Zhongbo Zhu, Zijia Chen   +209 more
semanticscholar   +1 more source