7 papers
Disentangling 3D Modeling from Spatial Reasoning
Haoze Sun, Jiequan Cui, Qingshan Xu +1
In this work, we explore an alternative paradigm for spatial reasoning by explicitly disentangling 3D perception from reasoning, rather than jointly acquiring implicit 3D perceptio…
Visual Token Compression Enhances Robustness of MLLMs
Shishen Gu, Jiequan Cui, Wenbo Hu +3
In this paper, we show for the first time that visual token pruning enhances the robustness of Multimodal Large Language Models (MLLMs), mitigating vulnerabilities such as jailbrea…
Class-frequency Guided Noise Schedule for Diffusion Models
Jiequan Cui, Beier Zhu, Qingshan Xu +3
In this paper, we are the first to examine the correlations between class frequency and the multi-scale noise schedule within diffusion models. For score-based generative models, l…
Rethinking VLMs for Image Forgery Detection and Localization
Shaofeng Guo, Jiequan Cui, Richang Hong
With the rapid rise of Artificial Intelligence Generated Content (AIGC), image manipulation has become increasingly accessible, posing significant challenges for image forgery dete…
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…