4 papers
FusionSAM: Visual Multi-Modal Learning with Segment Anything
Daixun Li, Weiying Xie, Mingxiang Cao +5
Multimodal image fusion and semantic segmentation are critical for autonomous driving. Despite advancements, current models often struggle with segmenting densely packed elements d…
Mamba: CLIP-driven Mamba Model for Multi-modal Remote Sensing Classification
Mingxiang Cao, Weiying Xie, Xin Zhang +4
Multi-modal fusion holds great promise for integrating information from different modalities. However, due to a lack of consideration for modal consistency, existing multi-modal fu…
Diffusion Models Meet Remote Sensing: Principles, Methods, and Perspectives
Yidan Liu, Jun Yue, Shaobo Xia +3
As a newly emerging advance in deep generative models, diffusion models have achieved state-of-the-art results in many fields, including computer vision, natural language processin…
Hyperspectral Anomaly Detection with Self-Supervised Anomaly Prior
Yidan Liu, Weiying Xie, Kai Jiang +3
The majority of existing hyperspectral anomaly detection (HAD) methods use the low-rank representation (LRR) model to separate the background and anomaly components, where the anom…