Search Results for author: Congcong Zhu

Found 9 papers, 6 papers with code

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models

1 code implementation16 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.

Self-Supervised Learning

STSA: Spatial-Temporal Semantic Alignment for Visual Dubbing

1 code implementation29 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.

Reinforcement Unlearning

1 code implementation26 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.

Inference Attack Machine Unlearning +2

Phase Matching for Out-of-Distribution Generalization

no code implementations24 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).

Contrastive Learning Domain Generalization +2

Unsupervised Learning of Multi-level Structures for Anomaly Detection

no code implementations25 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.

All Anomaly Detection

Towards Omni-Supervised Face Alignment for Large Scale Unlabeled Videos

no code implementations16 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.

Face Alignment Relational Reasoning

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