57 citations · 94 across the 4 of their papers we have counts for
6 papers
EigenGAN: Layer-Wise Eigen-Learning for GANs
Zhenliang He, Meina Kan, Shiguang Shan
Recent studies on Generative Adversarial Network (GAN) reveal that different layers of a generative CNN hold different semantics of the synthesized images. However, few GAN models…
PA-GAN: Progressive Attention Generative Adversarial Network for Facial Attribute Editing
Zhenliang He, Meina Kan, Jichao Zhang +1
Facial attribute editing aims to manipulate attributes on the human face, e.g., adding a mustache or changing the hair color. Existing approaches suffer from a serious compromise b…
Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation
Yude Wang, Jie Zhang, Meina Kan +2
Image-level weakly supervised semantic segmentation is a challenging problem that has been deeply studied in recent years. Most of advanced solutions exploit class activation map (…
Self-supervised Scale Equivariant Network for Weakly Supervised Semantic Segmentation
Yude Wang, Jie Zhang, Meina Kan +2
Weakly supervised semantic segmentation has attracted much research interest in recent years considering its advantage of low labeling cost. Most of the advanced algorithms follow…
Weakly Supervised Object Detection with Segmentation Collaboration
Xiaoyan Li, Meina Kan, Shiguang Shan +1
Weakly supervised object detection aims at learning precise object detectors, given image category labels. In recent prevailing works, this problem is generally formulated as a mul…
Fully Learnable Group Convolution for Acceleration of Deep Neural Networks
Xijun Wang, Meina Kan, Shiguang Shan +1
Benefitted from its great success on many tasks, deep learning is increasingly used on low-computational-cost devices, e.g. smartphone, embedded devices, etc. To reduce the high co…