7 papers
Reducing Class-Wise Performance Disparity via Margin Regularization
Beier Zhu, Kesen Zhao, Jiequan Cui +4
Deep neural networks often exhibit substantial disparities in class-wise accuracy, even when trained on class-balanced data, posing concerns for reliable deployment. While prior ef…
NeuSpring: Neural Spring Fields for Reconstruction and Simulation of Deformable Objects from Videos
Qingshan Xu, Jiao Liu, Shangshu Yu +6
In this paper, we aim to create physical digital twins of deformable objects under interaction. Existing methods focus more on the physical learning of current state modeling, but…
DragNeXt: Rethinking Drag-Based Image Editing
Yuan Zhou, Junbao Zhou, Qingshan Xu +5
Drag-Based Image Editing (DBIE), which allows users to manipulate images by directly dragging objects within them, has recently attracted much attention from the community. However…
On Path to Multimodal Generalist: General-Level and General-Bench
Hao Fei, Yuan Zhou, Juncheng Li +29
The Multimodal Large Language Model (MLLM) is currently experiencing rapid growth, driven by the advanced capabilities of LLMs. Unlike earlier specialists, existing MLLMs are evolv…
Personalize Your Gaussian: Consistent 3D Scene Personalization from a Single Image
Yuxuan Wang, Xuanyu Yi, Qingshan Xu +3
Personalizing 3D scenes from a single reference image enables intuitive user-guided editing, which requires achieving both multi-view consistency across perspectives and referentia…
Pushing Rendering Boundaries: Hard Gaussian Splatting
Qingshan Xu, Jiequan Cui, Xuanyu Yi +4
3D Gaussian Splatting (3DGS) has demonstrated impressive Novel View Synthesis (NVS) results in a real-time rendering manner. During training, it relies heavily on the average magni…