9 citations · 11 across the 18 of their papers we have counts for
18 papers
Continuous Exposure-Time Modeling for Realistic Atmospheric Turbulence Synthesis
Junwei Zeng, Dong Liang, Sheng-Jun Huang +2
Atmospheric turbulence significantly degrades long-range imaging by introducing geometric warping and exposure-time-dependent blur, which adversely affects both visual quality and…
CloudMatch: Weak-to-Strong Consistency Learning for Semi-Supervised Cloud Detection
Jiayi Zhao, Changlu Chen, Jingsheng Li +2
Due to the high cost of annotating accurate pixel-level labels, semi-supervised learning has emerged as a promising approach for cloud detection. In this paper, we propose CloudMat…
CD-Mamba: Cloud detection with long-range spatial dependency modeling
Tianxiang Xue, Jiayi Zhao, Jingsheng Li +2
Remote sensing images are frequently obscured by cloud cover, posing significant challenges to data integrity and reliability. Effective cloud detection requires addressing both sh…
Frequency Regulation for Exposure Bias Mitigation in Diffusion Models
Meng Yu, Kun Zhan
Diffusion models exhibit impressive generative capabilities but are significantly impacted by exposure bias. In this paper, we make a key observation: the energy of predicted noisy…
Graph-Weighted Contrastive Learning for Semi-Supervised Hyperspectral Image Classification
Yuqing Zhang, Qi Han, Ligeng Wang +3
Most existing graph-based semi-supervised hyperspectral image classification methods rely on superpixel partitioning techniques. However, they suffer from misclassification of cert…
BS-Mamba for Black-Soil Area Detection On the Qinghai-Tibetan Plateau
Xuan Ma, Zewen Lv, Chengcai Ma +3
Extremely degraded grassland on the Qinghai-Tibetan Plateau (QTP) presents a significant environmental challenge due to overgrazing, climate change, and rodent activity, which degr…
Hierarchical Consensus Network for Multiview Feature Learning
Chengwei Xia, Chaoxi Niu, Kun Zhan
Multiview feature learning aims to learn discriminative features by integrating the distinct information in each view. However, most existing methods still face significant challen…
Lesion-aware network for diabetic retinopathy diagnosis
Xue Xia, Kun Zhan, Yuming Fang +2
Deep learning brought boosts to auto diabetic retinopathy (DR) diagnosis, thus, greatly helping ophthalmologists for early disease detection, which contributes to preventing diseas…
High-Resolution Cloud Detection Network
Jingsheng Li, Tianxiang Xue, Jiayi Zhao +4
The complexity of clouds, particularly in terms of texture detail at high resolutions, has not been well explored by most existing cloud detection networks. This paper introduces t…
Structure-Aware Consensus Network on Graphs with Few Labeled Nodes
Shuaike Xu, Xiaolin Zhang, Peng Zhang +1
Graph node classification with few labeled nodes presents significant challenges due to limited supervision. Conventional methods often exploit the graph in a transductive learning…
InfoMatch: Entropy Neural Estimation for Semi-Supervised Image Classification
Qi Han, Zhibo Tian, Chengwei Xia +1
Semi-supervised image classification, leveraging pseudo supervision and consistency regularization, has demonstrated remarkable success. However, the ongoing challenge lies in full…
Self-Contrastive Graph Diffusion Network
Yixian Ma, Kun Zhan
Augmentation techniques and sampling strategies are crucial in contrastive learning, but in most existing works, augmentation techniques require careful design, and their sampling…
Entropy Neural Estimation for Graph Contrastive Learning
Yixuan Ma, Xiaolin Zhang, Peng Zhang +1
Contrastive learning on graphs aims at extracting distinguishable high-level representations of nodes. In this paper, we theoretically illustrate that the entropy of a dataset can…
Improving Semi-Supervised Semantic Segmentation with Dual-Level Siamese Structure Network
Zhibo Tain, Xiaolin Zhang, Peng Zhang +1
Semi-supervised semantic segmentation (SSS) is an important task that utilizes both labeled and unlabeled data to reduce expenses on labeling training examples. However, the effect…
Curriculum Knowledge Switching for Pancreas Segmentation
Yumou Tang, Kun Zhan, Zhibo Tian +3
Pancreas segmentation is challenging due to the small proportion and highly changeable anatomical structure. It motivates us to propose a novel segmentation framework, namely Curri…
Stationary Diffusion State Neural Estimation for Multiview Clustering
Chenghua Liu, Zhuolin Liao, Yixuan Ma +1
Although many graph-based clustering methods attempt to model the stationary diffusion state in their objectives, their performance limits to using a predefined graph. We argue tha…
Mutual Teaching for Graph Convolutional Networks
Kun Zhan, Chaoxi Niu
Graph convolutional networks produce good predictions of unlabeled samples due to its transductive label propagation. Since samples have different predicted confidences, we take hi…
Generative approach to unsupervised deep local learning
Changlu Chen, Chaoxi Niu, Xia Zhan +1
Most existing feature learning methods optimize inflexible handcrafted features and the affinity matrix is constructed by shallow linear embedding methods. Different from these con…