Publications

publications by categories in reversed chronological order. generated by jekyll-scholar.

  1. teaser-physconvex.png
    Dan Wang, Xinrui Cui*, Serge Belongie, and Ravi Ramamoorthi
    arXiv, 2026
    A physics-informed framework that unifies visual rendering and physical simulation by representing deformable radiance fields as boundary-driven 3D dynamic convex primitives governed by reduced-order continuum mechanics, enabling the high-fidelity reconstruction of appearance, geometry, and physical properties.
  2. teaser-SR.png
    Dan Wang, Haiyan Sun, Shan Du, Z. Jane Wang, Zhaochong An, Serge Belongie, and Xinrui Cui*
    arXiv, 2026
    A spatial-semantic guided diffusion framework that integrates spatial-grounded textual guidance and semantic-enhanced visual guidance to achieve a superior balance in the perception-distortion trade-off, producing super-resolution results that are both perceptually realistic and structurally faithful.
  3. teaser-YXLiu.png
    Yuxuan Liu, Dan Wang, and Xinrui Cui*
    Coming soon, 2026
  4. flow-innerf.png
    Dan Wang and Xinrui Cui*
    In ACM International Conference on Multimedia, Melbourne VIC, Australia, 2024
    A unified, end-to-end Transformer-based framework for generalizable 3D scene representation and rendering that improves model interpretability and performance.
  5. flow-ZYZhang.png
    Ziye Zhang, Aiping Liu, Yikai Gao, Xinrui Cui, Ruobing Qian, and Xun Chen
    IEEE Transactions on Cognitive and Developmental Systems, 2023
  6. flow-YKGao.png
    Yikai Gao, Aiping Liu, Xinrui Cui, Ruobing Qian, and Xun Chen
    Computers in Biology and Medicine, 2022
  7. flow-evolt.png
    Dan Wang, Xinrui Cui*, Xun Chen, Zhengxia Zou, Tianyang Shi, Septimiu Salcudean, Z. Jane Wang, and Rabab Ward
    In International Conference on Computer Vision (ICCV Oral), Oct 2021
    A novel Transformer-based framework for multi-view 3D reconstruction that effectively captures long-range dependencies across views, leading to improved reconstruction quality and robustness.
  8. flow-chain.jpg
    Dan Wang, Xinrui Cui*, Xun Chen, Rabab Ward, and Z. Jane Wang
    IEEE Transactions on Image Processing, 2021
    An interpretation scheme that explains CNN decision-making by backwardly decomposing high-level semantic concepts into a hierarchy of lower-level visual concepts across different network layers, mimicking the bottom-up hierarchical logic of human visual recognition.
  9. fig2-chip.png
    Xinrui Cui, Dan Wang, and Z. Jane Wang
    IEEE Transactions on Neural Networks and Learning Systems, 2020
    A channel-wise disentangled interpretation method that identifies the most influential channels in a CNN for a given prediction and disentangles their contributions to different visual concepts, providing a more detailed and interpretable explanation of the model’s decision-making process.
  10. showcase-flowin.jpg
    Xinrui Cui, Dan Wang, and Z. Jane Wang
    IEEE Transactions on Multimedia, 2020
    A feature-flow interpretation method that learns the flow of information through a CNN by tracking the activation patterns of features across layers, revealing how different features contribute to the final prediction.
  11. model0-mint.jpg
    Xinrui Cui, Dan Wang, and Z. Jane Wang
    IEEE Transactions on Multimedia, 2019
    A multi-scale interpretation model that provides hierarchical explanations of CNN predictions by interpreting the model’s decision-making process at multiple levels of abstraction.
  12. fig-SPaUnM.png
    Dan Wang, Zhenwei Shi, and Xinrui Cui
    IEEE Transactions on Geoscience and Remote Sensing, 2018
    A robust sparse unmixing method for hyperspectral imagery that incorporates spatial information and a novel regularization term to improve the accuracy and robustness of unmixing results.
  13. flow-SLSeg.jpeg
    Dan Wang, Xinrui Cui, Fengying Xie, Zhiguo Jiang, and Zhenwei Shi
    International Journal of Remote Sensing, 2017
    A multi-feature sea–land segmentation method that leverages pixel-wise learning to improve the accuracy and robustness of optical remote-sensing imagery analysis.
  14. fig-globalsip.png
    Xinrui Cui and Z. Jane Wang
    In 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2017
  15. Xiaofang Liu, Xinrui Cui, Yaxin Chen, Xiao-Juan Zhang, Ronghai Yu, Guang-Sheng Wang, and Hua Ma
    Carbon, 2015
  16. Xiaofang Liu, Yaxin Chen, Xinrui Cui, Min Zeng, Ronghai Yu, and Guang-Sheng Wang
    Journal of Materials Chemistry A, 2015
  17. Xiaofang Liu, Xinrui Cui, Yiding Liu, and Yadong Yin
    Nanoscale, 2015
  18. Xiaofang Liu, Xiaobo Chen, Xinrui Cui, and Ronghai Yu
    Ceramics International, 2014
  19. Xiaofang Liu, Xinrui Cui, Xiaobo Chen, Na Yang, and Ronghai Yu
    Materials Research Bulletin, 2014