Abstract Complementary Label Learning (CLL), a crucial aspect of weakly supervised
learning, has seen significant theoretical and practical advancements. However, a …

Abstract Complementary Label Learning (CLL) is a typical weakly supervised learning
protocol, where each instance is associated with one complementary label to specify a class …

Loss functions and sample mining strategies are essential components in deep metric
learning algorithms. However, the existing loss function or mining strategy often necessitates …

While deep learning, particularly convolutional neural networks (CNNs), has revolutionized
remote sensing (RS) change detection (CD), existing approaches often miss crucial features …

深度监督学习在合成孔径雷达自动目标识别任务中的成功依赖于大量标签样本. 但是,
在大规模数据集中经常存在错误(噪声) 标签, 很大程度降低网络训练效果 …

Manually annotating datasets for training deep models is very labor-intensive and time-
consuming. To overcome such inferiority, directly leveraging web images to conduct training …

Loss functions and sample mining strategies are essential components in deep metric
learning algorithms. However, the existing loss function or mining strategy often necessitate …

Label noise is ubiquitous in real-world scenarios, posing a practical challenge to supervised
models due to its effect in hurting the generalization performance of deep neural networks …

Though adversarial erasing has prevailed in weakly supervised semantic segmentation to
help activate integral object regions, existing approaches still suffer from the dilemma of …

Matching visible and near-infrared (NIR) images remains a significant challenge in remote
sensing image fusion. The nonlinear radiometric differences between heterogeneous …