154 citations · 848 across the 33 of their papers we have counts for
8 papers · 1 filter
FaceFeat-GAN: a Two-Stage Approach for Identity-Preserving Face Synthesis
Yujun Shen, Bolei Zhou, Ping Luo +1
The advance of Generative Adversarial Networks (GANs) enables realistic face image synthesis. However, synthesizing face images that preserve facial identity as well as have high d…
Do Normalization Layers in a Deep ConvNet Really Need to Be Distinct?
Ping Luo, Zhanglin Peng, Jiamin Ren +1
Yes, they do. This work investigates a perspective for deep learning: whether different normalization layers in a ConvNet require different normalizers. This is the first step towa…
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
Xingang Pan, Ping Luo, Jianping Shi +1
Convolutional neural networks (CNNs) have achieved great successes in many computer vision problems. Unlike existing works that designed CNN architectures to improve performance on…
Talking Face Generation by Adversarially Disentangled Audio-Visual Representation
Hang Zhou, Yu Liu, Ziwei Liu +2
Talking face generation aims to synthesize a sequence of face images that correspond to a clip of speech. This is a challenging task because face appearance variation and semantics…
SCAN: Self-and-Collaborative Attention Network for Video Person Re-identification
Ruimao Zhang, Hongbin Sun, Jingyu Li +4
Video person re-identification attracts much attention in recent years. It aims to match image sequences of pedestrians from different camera views. Previous approaches usually imp…
Differentiable Learning-to-Normalize via Switchable Normalization
Ping Luo, Jiamin Ren, Zhanglin Peng +2
We address a learning-to-normalize problem by proposing Switchable Normalization (SN), which learns to select different normalizers for different normalization layers of a deep neu…