50 citations · 128 across the 13 of their papers we have counts for
13 papers
NukesFormers: Unpaired Hyperspectral Image Generation with Non-Uniform Domain Alignment
Jiaojiao Li, Shiyao Duan, Haitao XU +1
The inherent difficulty in acquiring accurately co-registered RGB-hyperspectral image (HSI) pairs has significantly impeded the practical deployment of current data-driven Hyperspe…
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…
An End-to-End Real-World Camera Imaging Pipeline
Kepeng Xu, Zijia Ma, Li Xu +5
Recent advances in neural camera imaging pipelines have demonstrated notable progress. Nevertheless, the real-world imaging pipeline still faces challenges including the lack of jo…
SeaDATE: Remedy Dual-Attention Transformer with Semantic Alignment via Contrast Learning for Multimodal Object Detection
Shuhan Dong, Yunsong Li, Weiying Xie +4
Multimodal object detection leverages diverse modal information to enhance the accuracy and robustness of detectors. By learning long-term dependencies, Transformer can effectively…
Unleashing the Power of Generic Segmentation Models: A Simple Baseline for Infrared Small Target Detection
Mingjin Zhang, Chi Zhang, Qiming Zhang +3
Recent advancements in deep learning have greatly advanced the field of infrared small object detection (IRSTD). Despite their remarkable success, a notable gap persists between th…
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…
Multimodal Informative ViT: Information Aggregation and Distribution for Hyperspectral and LiDAR Classification
Jiaqing Zhang, Jie Lei, Weiying Xie +3
In multimodal land cover classification (MLCC), a common challenge is the redundancy in data distribution, where irrelevant information from multiple modalities can hinder the effe…
SwiMDiff: Scene-wide Matching Contrastive Learning with Diffusion Constraint for Remote Sensing Image
Jiayuan Tian, Jie Lei, Jiaqing Zhang +2
With recent advancements in aerospace technology, the volume of unlabeled remote sensing image (RSI) data has increased dramatically. Effectively leveraging this data through self-…
Distribution-aware Interactive Attention Network and Large-scale Cloud Recognition Benchmark on FY-4A Satellite Image
Jiaqing Zhang, Jie Lei, Weiying Xie +3
Accurate cloud recognition and warning are crucial for various applications, including in-flight support, weather forecasting, and climate research. However, recent deep learning a…
FedDiff: Diffusion Model Driven Federated Learning for Multi-Modal and Multi-Clients
DaiXun Li, Weiying Xie, ZiXuan Wang +3
With the rapid development of imaging sensor technology in the field of remote sensing, multi-modal remote sensing data fusion has emerged as a crucial research direction for land…
RS-DGC: Exploring Neighborhood Statistics for Dynamic Gradient Compression on Remote Sensing Image Interpretation
Weiying Xie, Zixuan Wang, Jitao Ma +2
Distributed deep learning has recently been attracting more attention in remote sensing (RS) applications due to the challenges posed by the increased amount of open data that are…
FedFusion: Manifold Driven Federated Learning for Multi-satellite and Multi-modality Fusion
DaiXun Li, Weiying Xie, Yunsong Li +1
Multi-satellite, multi-modality in-orbit fusion is a challenging task as it explores the fusion representation of complex high-dimensional data under limited computational resource…
Guided Hybrid Quantization for Object detection in Multimodal Remote Sensing Imagery via One-to-one Self-teaching
Jiaqing Zhang, Jie Lei, Weiying Xie +2
Considering the computation complexity, we propose a Guided Hybrid Quantization with One-to-one Self-Teaching (GHOST}) framework. More concretely, we first design a structure calle…