1 code implementation • 16 May 2025 • Congcong Zhu, Xiaoyan Xu, Jiayue Han, Jingrun Chen
Auto-regressive partial differential equation (PDE) foundation models have shown great potential in handling time-dependent data.
1 code implementation • 29 Mar 2025 • Zijun Ding, Mingdie Xiong, Congcong Zhu, Jingrun Chen
Despite this, we observe that the semantic ambiguity between spatial and temporal domains significantly degrades the synthesis stability for the dynamic faces.
1 code implementation • 26 Dec 2023 • Dayong Ye, Tianqing Zhu, Congcong Zhu, Derui Wang, Kun Gao, Zewei Shi, Sheng Shen, Wanlei Zhou, Minhui Xue
Machine unlearning refers to the process of mitigating the influence of specific training data on machine learning models based on removal requests from data owners.
no code implementations • 24 Jul 2023 • Chengming Hu, Yeqian Du, Rui Wang, Hao Chen, Congcong Zhu
Beyond vanilla analysis and experiments, we further clarify the relationships between the Fourier components and DG problems by introducing a Fourier-based Structural Causal Model (SCM).
1 code implementation • CVPR 2022 • Congcong Zhu, Xintong Wan, Shaorong Xie, Xiaoqiang Li, Yinzheng Gu
The occlusion problem heavily degrades the localization performance of face alignment.
Ranked #3 on
Face Alignment
on COFW-68
1 code implementation • 19 Dec 2021 • Congcong Zhu, Xiaoqiang Li, Jide Li, Songmin Dai, Weiqin Tong
Moreover, the SRN augments the training data by synthesizing occluded faces.
no code implementations • 25 Apr 2021 • Songmin Dai, Jide Li, Lu Wang, Congcong Zhu, Yifan Wu, Xiaoqiang Li
This paper first introduces a novel method to generate anomalous data by breaking up global structures while preserving local structures of normal data at multiple levels.
1 code implementation • ICCV 2021 • Congcong Zhu, Xiaoqiang Li, Jide Li, Songmin Dai
We argue that exploring the weaknesses of the detector so as to remedy them is a promising method of robust facial landmark detection.
Ranked #5 on
Face Alignment
on COFW-68
no code implementations • 16 Dec 2019 • Congcong Zhu, Hao liu, Zhenhua Yu, Xuehong Sun
In this paper, we propose a spatial-temporal relational reasoning networks (STRRN) approach to investigate the problem of omni-supervised face alignment in videos.