diff --git a/research.html b/research.html index 6eb9420..dfbc0dd 100644 --- a/research.html +++ b/research.html @@ -94,7 +94,7 @@

Causal Representation Learning

Generative Models

- + Generative models, including GPT, GANs, and diffusion models, wield significant neural network capabilities to faithfully replicate intricate distributions found in real data. Our research emphasis lies in crafting generative models that are not only data-efficient but also computationally efficient. Moreover, delving into the realm of causal generative models, our core interest centers on developing models that mimic the data generation process while affording controllable and nuanced generations. Lastly, our curiosity extends to exploring generative models tailored for diverse data types, spanning images, text, 3D human motion, and beyond.
@@ -126,7 +126,7 @@

Generative Models

3D Vision

- + The realm of 3D vision is dedicated to modeling and comprehending the visual world, with a primary focus on deducing three-dimensional structures from two-dimensional images. Our research explores machine learning approaches for various facets of 3D vision, including depth estimation, novel view synthesis, feature matching, SLAM, and 3D model retrieval.
diff --git a/topics.html b/topics.html index ee17df0..76410de 100644 --- a/topics.html +++ b/topics.html @@ -1,3 +1,81 @@ +

Causal Discovery & Inference

+ +

Causal Representation Learning (Transfer, Robustness, Fairness, etc)

-

Causal Discovery & Inference

- +

Generative Models

- +

  • + HuTuMotion: Human-Tuned Navigation of Latent Motion Diffusion Models with Minimal Feedback. [PDF]
    + G. Han, S. Huang, M. Gong, and J. Tang.
    + In AAAI, 2024. +

    • +

  • + Freetalker: Controllable Speech and Text-Driven Gesture Generation Based on Diffusion Models for Enhanced Speaker Naturalness. [PDF]
    + S. Yang, Z. Xu, H. Xue, Y. Cheng, S. Huang, M. Gong, and Z. Wu.
    + In ICASSP, 2024. +

    • +

  • + Semi-Implicit Denoising Diffusion Models (SIDDMs). [PDF]
    + Y. Xu, M. Gong, S. Xie, W. Wei, M. Grundmann, K. Batmanghelich*, T. Hou*.
    + In NeurIPS, 2023.
    + High-quality image generation in a few diffusion steps, an extension to text-to-image generation is here. +

    • +

  • + Unpaired Image-to-Image Translation with Shortest Path Regularization. [PDF]
    + S. Xie, Y. Xu, M. Gong, and K. Zhang.
    + In CVPR, 2023. +

    • +

  • + Multi-Domain Image Generation and Translation with Identifiability Guarantees. [PDF][CODE]
    + S. Xie, L. Kong, M. Gong, and K. Zhang.
    + In ICLR, 2023. (Spotlight) +

    • +

  • + Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency Constraint. [PDF] [CODE]
    + J. Guo, J. Li, H. Fu, M. Gong, K. Zhang, D. Tao.
    + In CVPR, 2022. +

    • +

  • + Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation. [PDF] [CODE]
    + Y. Xu, S. Xie, W. Wu, K. Zhang, M. Gong*, K. Batmanghelich*.
    + In CVPR, 2022. +

    • +

  • + Few-Shot Font Generation by Learning Fine-Grained Local Styles. [PDF]
    + L. Tang, Y. Cai, J. Liu, Z. Hong, M. Gong, M. Fan, J. Han, J. L, E. Ding, J. Wang.
    + In CVPR, 2022. +

    • +

  • + Hierarchical Amortized GAN for 3D High Resolution Medical Image Synthesis. [PDF][CODE]
    + L. Sun, J. Chen, Y. Xu, M. Gong, K. Yu, K. Batmanghelich
    + IEEE JBHI, (2022). +

    • +

  • + Unaligned Image-to-Image Translation by Learning to Reweight. [PDF] [CODE]
    + S. Xie, M. Gong, Y. Xu, and K. Zhang.
    + In ICCV, 2021. +

    • +

  • + Twin Auxiliary Classifiers GAN. [PDF][CODE]
    + M. Gong*, Y. Xu*, C. Li, K. Zhang, and K. Batmanghelich.
    + In NeurIPS, 2019. (Spotlight, acceptance rate 2.4%) +

    • +

  • + Geometry-Consistent Adversarial Networks for Unsupervised Domain Mapping. [PDF][CODE]
    + H. Fu*, M. Gong*, C. Wang, K. Batmanghelich, K. Zhang, and D. Tao.
    + In CVPR, 2019. (best paper finalist, top 1%)
    +

    • + +

    3D Vision

    - -

    Generative Models

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