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Congcong Zhu

7 accepted papers

2026

MeshTok: Efficient Multi-Scale Tokenization for Scalable PDE Transformers

ICML 2026poster

Conventional patchified Transformers operate on uniform spatial partitions, distributing computational effort evenly across the domain irrespective of local features. This inflexible tokenization scheme is inherently limited in its ability to efficiently represent and process solutions to complex PD…

Cited by 0SourceScholar
2026

Physics-Informed Deformable Gaussian Splatting: Towards Unified Constitutive Laws for Time-Evolving Material Field

AAAI 2026technical

Recently, 3D Gaussian Splatting (3DGS), an explicit scene representation technique, has shown significant promise for dynamic novel-view synthesis from monocular video input. However, purely data-driven 3DGS often struggles to capture the diverse physics-driven motion patterns in dynamic scenes. To

Cited by 0SourcePDFScholar
2026

Rethinking Bias in Generative Data Augmentation for Medical AI: A Frequency Recalibration Method

AAAI 2026technical

Developing Medical AI relies on large datasets and easily suffers from data scarcity. Generative data augmentation (GDA) using AI generative models offers a solution to synthesize realistic medical images. However, the bias in GDA is often underestimated in medical domains, with concerns about the r

Cited by 0SourcePDFScholar
2025

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

ICML 2025poster

Auto-regressive partial differential equation (PDE) foundation models have shown great potential in handling time-dependent data. However, these models suffer from error accumulation caused by the shortcut problem deeply rooted in auto-regressive prediction. The challenge becomes particularly eviden…

2022

Occlusion-Robust Face Alignment Using a Viewpoint-Invariant Hierarchical Network Architecture

CVPR 2022oral

The occlusion problem heavily degrades the localization performance of face alignment. Most current solutions for this problem focus on annotating new occlusion data, introducing boundary estimation, and stacking deeper models to improve the robustness of neural networks. However, the performance de…

Cited by 18PDFcodeScholar
2021

Improving Robustness of Facial Landmark Detection by Defending Against Adversarial Attacks

ICCV 2021poster

Many recent developments in facial landmark detection have been driven by stacking model parameters or augmenting annotations. However, three subsequent challenges remain, including 1) an increase in computational overhead, 2) the risk of overfitting caused by increasing model parameters, and 3) the…

Cited by 35PDFcodeScholar