684 citations · 988 across the 6 of their papers we have counts for
6 papers
Integrating Large Pre-trained Models into Multimodal Named Entity Recognition with Evidential Fusion
Weide Liu, Xiaoyang Zhong, Jingwen Hou +3
Multimodal Named Entity Recognition (MNER) is a crucial task for information extraction from social media platforms such as Twitter. Most current methods rely on attention weights…
Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Images
Haifeng Li, Yi Li, Guo Zhang +4
Supervised learning for semantic segmentation requires a large number of labeled samples, which is difficult to obtain in the field of remote sensing. Self-supervised learning (SSL…
RS-MetaNet: Deep meta metric learning for few-shot remote sensing scene classification
Haifeng Li, Zhenqi Cui, Zhiqing Zhu +4
Training a modern deep neural network on massive labeled samples is the main paradigm in solving the scene classification problem for remote sensing, but learning from only a few d…
DAPnet: A Double Self-attention Convolutional Network for Point Cloud Semantic Labeling
Li Chen, Zewei Xu, Yongjian Fu +3
Airborne Laser Scanning (ALS) point clouds have complex structures, and their 3D semantic labeling has been a challenging task. It has three problems: (1) the difficulty of classif…
DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images
Jie Chen, Ziyang Yuan, Jian Peng +5
Change detection is a basic task of remote sensing image processing. The research objective is to identity the change information of interest and filter out the irrelevant change i…
Convolution Neural Network Architecture Learning for Remote Sensing Scene Classification
Jie Chen, Haozhe Huang, Jian Peng +5
Remote sensing image scene classification is a fundamental but challenging task in understanding remote sensing images. Recently, deep learning-based methods, especially convolutio…