all AI news
HSIDMamba: Exploring Bidirectional State-Space Models for Hyperspectral Denoising
April 16, 2024, 4:48 a.m. | Yang Liu, Jiahua Xiao, Yu Guo, Peilin Jiang, Haiwei Yang, Fei Wang
cs.CV updates on arXiv.org arxiv.org
Abstract: Effectively discerning spatial-spectral dependencies in HSI denoising is crucial, but prevailing methods using convolution or transformers still face computational efficiency limitations. Recently, the emerging Selective State Space Model(Mamba) has risen with its nearly linear computational complexity in processing natural language sequences, which inspired us to explore its potential in handling long spectral sequences. In this paper, we propose HSIDMamba(HSDM), tailored to exploit the linear complexity for effectively capturing spatial-spectral dependencies in HSI denoising. In particular, …
abstract arxiv complexity computational convolution cs.cv denoising dependencies efficiency explore face language limitations linear mamba natural natural language processing space spatial state state space model transformers type
More from arxiv.org / cs.CV updates on arXiv.org
Jobs in AI, ML, Big Data
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Sr. VBI Developer II
@ Atos | Texas, US, 75093
Wealth Management - Data Analytics Intern/Co-op Fall 2024
@ Scotiabank | Toronto, ON, CA