Fast Projected Fuzzy Clustering With Anchor Guidance for Multimodal Remote Sensing Imagery [0.03%]
带有锚点引导的快速投影模糊C均值聚类在多模态遥感图像上的应用
Yongshan Zhang,Shuaikang Yan,Lefei Zhang et al.
Yongshan Zhang et al.
Multimodal remote sensing image recognition is a popular research topic in the field of remote sensing. This recognition task is mostly solved by supervised learning methods that heavily rely on manually labeled data. When the labels are ab...
Low Overlapping Point Cloud Registration Using Mutual Prior Based Completion Network [0.03%]
基于互 pri 的先验完成网络的低重叠点云注册
Yazhou Liu,Zhiyong Liu
Yazhou Liu
This work presents a new completion method that specifically designed for low-overlapping partial point cloud registration. Based on the assumption that the candidate partial point clouds to be registered belong to the same target, the prop...
Adaptive Prototype Learning for Weakly-supervised Temporal Action Localization [0.03%]
弱监督视频动作定位的自适应原型学习方法
Wang Luo,Huan Ren,Tianzhu Zhangd et al.
Wang Luo et al.
Weakly-supervised Temporal Action Localization (WTAL) aims to localize action instances with only video-level labels during training, where two primary issues are localization incompleteness and background interference. To relieve these two...
Lightweight Prompt Learning Implicit Degradation Estimation Network for Blind Super Resolution [0.03%]
轻量级提示学习盲式超分辨率隐式退化估计网络
Asif Hussain Khan,Christian Micheloni,Niki Martinel
Asif Hussain Khan
Blind image super-resolution (SR) aims to recover a high-resolution (HR) image from its low-resolution (LR) counterpart under the assumption of unknown degradations. Many existing blind SR methods rely on supervising ground-truth kernels re...
A Trustworthy Counterfactual Explanation Method With Latent Space Smoothing [0.03%]
一种基于潜在空间平滑的可信任反事实解释方法
Yan Li,Xia Cai,Chunwei Wu et al.
Yan Li et al.
Despite the large-scale adoption of Artificial Intelligence (AI) models in healthcare, there is an urgent need for trustworthy tools to rigorously backtrack the model decisions so that they behave reliably. Counterfactual explanations take ...
Deep Cross-View Reconstruction GAN Based on Correlated Subspace for Multi-View Transformation [0.03%]
基于相关子空间的深度跨视图重建GAN的多视图变换方法
Jian-Xun Mi,Junchang He,Weisheng Li
Jian-Xun Mi
In scenarios where identifying face information in the visible spectrum (VIS) is challenging due to poor lighting conditions, the use of near-infrared (NIR) and thermal (TH) cameras can provide viable alternatives. However, the unique data ...
Linear Combinations of Patches are Unreasonably Effective for Single-Image Denoising [0.03%]
基于图像块线性组合的去噪方法的有效性研究
Sebastien Herbreteau,Charles Kervrann
Sebastien Herbreteau
In the past decade, deep neural networks have revolutionized image denoising in achieving significant accuracy improvements by learning on datasets composed of noisy/clean image pairs. However, this strategy is extremely dependent on traini...
Minimalist and High-Quality Panoramic Imaging With PSF-Aware Transformers [0.03%]
基于认识变换的极简主义和高质量全景图影像技术
Qi Jiang,Shaohua Gao,Yao Gao et al.
Qi Jiang et al.
High-quality panoramic images with a Field of View (FoV) of 360° are essential for contemporary panoramic computer vision tasks. However, conventional imaging systems come with sophisticated lens designs and heavy optical components. This ...
Multi-Granularity Part Sampling Attention for Fine-Grained Visual Classification [0.03%]
多粒度部件采样注意力的细粒度图像分类方法
Jiahui Wang,Qin Xu,Bo Jiang et al.
Jiahui Wang et al.
Fine-grained visual classification aims to classify similar sub-categories with the challenges of large variations within the same sub-category and high visual similarities between different sub-categories. Recently, methods that extract se...
Semi-Supervised Semantic Segmentation for Light Field Images Using Disparity Information [0.03%]
基于视差信息的光场图像半监督语义分割
Shansi Zhang,Yaping Zhao,Edmund Y Lam
Shansi Zhang
Light field (LF) images enable numerous applications due to their ability to capture information for multiple views. Semantic segmentation is an essential task for LF scene understanding. However, existing supervised methods heavily rely on...