2 citations · 4 across the 5 of their papers we have counts for
5 papers
MBTI: A Multi-Branch Efficient Fine-Tuning Framework for Hyperspectral Image Classification with Foundation Models
Mingzhen Xu, Haonan Guo, Di Wang +9
Hyperspectral foundation models learn transferable spectral-spatial representations from large-scale unlabeled data. They provide an effective paradigm for adapting to downstream h…
MambaMoE: Mixture-of-Spectral-Spatial-Experts State Space Model for Hyperspectral Image Classification
Yichu Xu, Di Wang, Hongzan Jiao +2
Mamba-based models have recently demonstrated significant potential in hyperspectral image (HSI) classification, primarily due to their ability to perform contextual modeling with…
Model Hemorrhage and the Robustness Limits of Large Language Models
Ziyang Ma, Zuchao Li, Lefei Zhang +4
Large language models (LLMs) demonstrate strong performance across natural language processing tasks, yet undergo significant performance degradation when modified for deployment t…
HSRMamba: Contextual Spatial-Spectral State Space Model for Single Image Hyperspectral Super-Resolution
Shi Chen, Lefei Zhang, Liangpei Zhang
Mamba has demonstrated exceptional performance in visual tasks due to its powerful global modeling capabilities and linear computational complexity, offering considerable potential…
Dual Selective Fusion Transformer Network for Hyperspectral Image Classification
Yichu Xu, Di Wang, Lefei Zhang +1
Transformer has achieved satisfactory results in the field of hyperspectral image (HSI) classification. However, existing Transformer models face two key challenges when dealing wi…