176 citations · 429 across the 12 of their papers we have counts for
13 papers
Remote Sensing Image Intelligent Interpretation with the Language-Centered Perspective: Principles, Methods and Challenges
Haifeng Li, Wang Guo, Haiyang Wu +6
The mainstream paradigm of remote sensing image interpretation has long been dominated by vision-centered models, which rely on visual features for semantic understanding. However,…
BCE-Net: Reliable Building Footprints Change Extraction based on Historical Map and Up-to-Date Images using Contrastive Learning
Cheng Liao, Han Hu, Xuekun Yuan +6
Automatic and periodic recompiling of building databases with up-to-date high-resolution images has become a critical requirement for rapidly developing urban environments. However…
Self-supervised remote sensing feature learning: Learning Paradigms, Challenges, and Future Works
Chao Tao, Ji Qi, Mingning Guo +2
Deep learning has achieved great success in learning features from massive remote sensing images (RSIs). To better understand the connection between feature learning paradigms (e.g…
TOV: The Original Vision Model for Optical Remote Sensing Image Understanding via Self-supervised Learning
Chao Tao, Ji Qia, Guo Zhang +3
Do we on the right way for remote sensing image understanding (RSIU) by training models via supervised data-dependent and task-dependent way, instead of human vision in a label-fre…
Reviewing continual learning from the perspective of human-level intelligence
Yifan Chang, Wenbo Li, Jian Peng +7
Humans' continual learning (CL) ability is closely related to Stability Versus Plasticity Dilemma that describes how humans achieve ongoing learning capacity and preservation for l…
Learning by Active Forgetting for Neural Networks
Jian Peng, Xian Sun, Min Deng +8
Remembering and forgetting mechanisms are two sides of the same coin in a human learning-memory system. Inspired by human brain memory mechanisms, modern machine learning systems h…
Curvature Graph Neural Network
Haifeng Li, Jun Cao, Jiawei Zhu +3
Graph neural networks (GNNs) have achieved great success in many graph-based tasks. Much work is dedicated to empowering GNNs with the adaptive locality ability, which enables meas…
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…
Graph Information Vanishing Phenomenon inImplicit Graph Neural Networks
Haifeng Li, Jun Cao, Jiawei Zhu +2
One of the key problems of GNNs is how to describe the importance of neighbor nodes in the aggregation process for learning node representations. A class of GNNs solves this proble…
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…
Overcoming Catastrophic Forgetting by Soft Parameter Pruning
Jian Peng, Jiang Hao, Zhuo Li +5
Catastrophic forgetting is a challenge issue in continual learning when a deep neural network forgets the knowledge acquired from the former task after learning on subsequent tasks…
Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection
Enqiang Guo, Xinsha Fu, Jiawei Zhu +4
A critical challenge problem of scene change detection is that noisy changes generated by varying illumination, shadows and camera viewpoint make variances of a scene difficult to…
Measuring Road Network Topology Vulnerability by Ricci Curvature
Lei Gao, Xingquan Liu, Yu Liu +4
Describing the basic properties of road network systems, such as their robustness, vulnerability, and reliability, has been a very important research topic in the field of urban tr…