no code implementations • 15 Jan 2024 • Jie Zhang, Zhifan Wan, Lanqing Hu, Stephen Lin, Shuzhe Wu, Shiguang Shan
Considering the close connection between action recognition and human pose estimation, we design a Collaboratively Self-supervised Video Representation (CSVR) learning framework specific to action recognition by jointly considering generative pose prediction and discriminative context matching as pretext tasks.
no code implementations • 19 Aug 2022 • Changzhen Li, Jie Zhang, Shuzhe Wu, Xin Jin, Shiguang Shan
Recently action recognition has received more and more attention for its comprehensive and practical applications in intelligent surveillance and human-computer interaction.
1 code implementation • CVPR 2022 • Guanqi Ding, Xinzhe Han, Shuhui Wang, Shuzhe Wu, Xin Jin, Dandan Tu, Qingming Huang
Few-shot image generation is a challenging task even using the state-of-the-art Generative Adversarial Networks (GANs).
no code implementations • 27 Sep 2018 • Chunrui Han, Shiguang Shan, Meina Kan, Shuzhe Wu, Xilin Chen
Specifically, we introduce a kernel generator as meta-learner to learn to construct feature embedding for query images.
no code implementations • ECCV 2018 • Chunrui Han, Shiguang Shan, Meina Kan, Shuzhe Wu, Xilin Chen
In current face recognition approaches with convolutional neural network (CNN), a pair of faces to compare are independently fed into the CNN for feature extraction.
1 code implementation • CVPR 2018 • Xuepeng Shi, Shiguang Shan, Meina Kan, Shuzhe Wu, Xilin Chen
Rotation-invariant face detection, i. e. detecting faces with arbitrary rotation-in-plane (RIP) angles, is widely required in unconstrained applications but still remains as a challenging task, due to the large variations of face appearances.
no code implementations • 23 Sep 2016 • Shuzhe Wu, Meina Kan, Zhenliang He, Shiguang Shan, Xilin Chen
On the other hand, by using a unified MLP cascade to examine proposals of all views in a centralized style, it provides a favorable solution for multi-view face detection with high accuracy and low time-cost.
no code implementations • ICCV Workshop 2015 • Xin Liu, Shaoxin Li, Meina Kan, Jie Zhang, Shuzhe Wu, Wenxian Liu, Hu Han, Shiguang Shan, Xilin Chen
Another key feature of the proposed AgeNet is that, to avoid the problem of over-fitting on small apparent age training set, we exploit a general-to-specific transfer learning scheme.
Ranked #4 on Age Estimation on ChaLearn 2015