52 citations · 102 across the 12 of their papers we have counts for
12 papers
Vector Map as Language: Toward Unified Remote Sensing Vector Mapping
Yinglong Yan, Yunkai Yang, Haoyi Wang +7
Remote sensing vector mapping aims to generate structured maps of geospatial entities, such as buildings, roads, and water bodies, from remote sensing imagery. In practice, vector…
High-Energy Concentration for Federated Learning in Frequency Domain
Haozhi Shi, Weiying Xie, Hangyu Ye +4
Federated Learning (FL) presents significant potential for collaborative optimization without data sharing. Since synthetic data is sent to the server, leveraging the popular conce…
HeteroTune: Efficient Federated Learning for Large Heterogeneous Models
Ruofan Jia, Weiying Xie, Jie Lei +3
While large pre-trained models have achieved impressive performance across AI tasks, their deployment in privacy-sensitive and distributed environments remains challenging. Federat…
SurANet: Surrounding-Aware Network for Concealed Object Detection via Highly-Efficient Interactive Contrastive Learning Strategy
Yuhan Kang, Qingpeng Li, Leyuan Fang +2
Concealed object detection (COD) in cluttered scenes is significant for various image processing applications. However, due to that concealed objects are always similar to their ba…
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…
GraphMamba: An Efficient Graph Structure Learning Vision Mamba for Hyperspectral Image Classification
Aitao Yang, Min Li, Yao Ding +3
Efficient extraction of spectral sequences and geospatial information has always been a hot topic in hyperspectral image classification. In terms of spectral sequence feature captu…
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…
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…
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…
Weakly Supervised Point Cloud Segmentation via Conservative Propagation of Scene-level Labels
Shaobo Xia, Jun Yue, Kacper Kania +4
We propose a weakly supervised semantic segmentation method for point clouds that predicts "per-point" labels from just "whole-scene" annotations. The key challenge here is the dis…
Physics Inspired Criterion for Pruning-Quantization Joint Learning
Weiying Xie, Xiaoyi Fan, Xin Zhang +3
Pruning-quantization joint learning always facilitates the deployment of deep neural networks (DNNs) on resource-constrained edge devices. However, most existing methods do not joi…
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…