1 citations · 1 across the 3 of their papers we have counts for
4 papers
Relating CNN-Transformer Fusion Network for Change Detection
Yuhao Gao, Gensheng Pei, Mengmeng Sheng +3
While deep learning, particularly convolutional neural networks (CNNs), has revolutionized remote sensing (RS) change detection (CD), existing approaches often miss crucial feature…
Foster Adaptivity and Balance in Learning with Noisy Labels
Mengmeng Sheng, Zeren Sun, Tao Chen +3
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 netw…
Learning with Imbalanced Noisy Data by Preventing Bias in Sample Selection
Huafeng Liu, Mengmeng Sheng, Zeren Sun +3
Learning with noisy labels has gained increasing attention because the inevitable imperfect labels in real-world scenarios can substantially hurt the deep model performance. Recent…
Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label Learning
Mengmeng Sheng, Zeren Sun, Zhenhuang Cai +3
There has been significant attention devoted to the effectiveness of various domains, such as semi-supervised learning, contrastive learning, and meta-learning, in enhancing the pe…