3D Face Modeling | 3D Object Detection | 3D Shape Correspondence | 3D Shape Reconstruction | Activity Recognition | Application | Biometrics | Camera Calibration | Camera+LiDAR+Radar | Data Imputation | Database | Depth Completion | Depth Prediction | Domain Adaptation | Expression Recognition | Face Alignment | Face Antispoofing | Face Deidentification | Face Recognition | Face Reconstruction | Face Relighting | Face Synthesis | Forecasting | Gait Recognition | Generic Object 3D Reconstruction | Image Alignment | Image Manipulation | Image Segmentation | Low-level Vision | Medical Imaging | Motion Compensation | Multi-modality | Multimedia Retrieval | Object Detection | Pedestrian Detection | Plant Vision | Semantic Segmentation | Surveillance | Tracking | Typing Behavior

Semantic Segmentation

  1. summary image

    Fully Understanding Generic Objects: Modeling, Segmentation, and Reconstruction

    Feng Liu, Luan Tran, Xiaoming Liu

    Inferring 3D structure of a generic object from a 2D image is a long-standing objective of computer vision. Conventional approaches either learn completely from CAD-generated synthetic data, which have difficulty in inference from real images, or generate 2.5D depth image via intrinsic decomposition, which is limited compared to the ...

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    Keywords: Generic Object 3D Reconstruction, 3D Shape Reconstruction, Semantic Segmentation

  2. summary image

    The Edge of Depth: Explicit Constraints between Segmentation and Depth

    Shengjie Zhu, Garrick Brazil, Xiaoming Liu

    In this work we study the mutual benefits of two common computer vision tasks, self-supervised depth estimation and semantic segmentation from images. For example, to help unsupervised monocular depth estimation, constraints from semantic segmentation has been explored implicitly such as sharing and transforming features. In contrast, we propose to explicitly ...

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    Keywords: Depth Prediction, Image Segmentation, Semantic Segmentation

  3. summary image

    Recurrent Flow-Guided Semantic Forecasting

    Adam M. Terwilliger, Garrick Brazil, Xiaoming Liu

    Understanding the world around us and making decisions about the future is a critical component to human intelligence. As autonomous systems continue to develop, their ability to reason about the future will be the key to their success. Semantic anticipation is a relatively under-explored area for which autonomous vehicles could ...

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    Keywords: Forecasting, Semantic Segmentation, Image Segmentation

  4. summary image

    Illuminating Pedestrians via Simultaneous Detection & Segmentation

    Garrick Brazil, Xi Yin, Xiaoming Liu

    Pedestrian detection is a critical problem in computer vision with significant impact on safety in urban autonomous driving. In this work, we explore how semantic segmentation can be used to boost pedestrian detection accuracy while having little to no impact on network efficiency. We propose a segmentation infusion network to ...

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    Keywords: Pedestrian Detection, Semantic Segmentation, Object Detection, Image Segmentation

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    Image Segmentation of Mesenchymal Stem Cells in Diverse Culturing Conditions

    Muhammad Jamal Afridi, Chun Liu, Christina Chan, Seungik Baek, Xiaoming Liu

    Researchers in the areas of regenerative medicine and tissue engineering have an enormous interest in understanding the relationship of different sets of culturing conditions and applied mechanical stimuli on the behavior of Mesenchymal Stem Cells (MSCs). However, it remains a challenge to design a general tool to perform automatic cell ...

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    Keywords: Medical Imaging, Image Segmentation, Application, Semantic Segmentation

2022

  • On Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence
    Feng Liu, Xiaoming Liu
    IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), , Nov. 2022 (in press)
    Bibtex
  • @article{ on-learning-implicit-functions-for-topology-varying-dense-3d-shape-correspondence,
      author = { Feng Liu and Xiaoming Liu },
      title = { On Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence },
      journal = { IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) },
      month = { November },
      year = { 2022 },
    }

2021

  • Fully Understanding Generic Objects: Modeling, Segmentation, and Reconstruction
    Feng Liu, Luan Tran, Xiaoming Liu
    In Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2021), Nashville, TN, Jun. 2021
    Bibtex | PDF | arXiv | Supplemental | Project Webpage | Code | Video
  • @inproceedings{ fully-understanding-generic-objects-modeling-segmentation-and-reconstruction,
      author = { Feng Liu and Luan Tran and Xiaoming Liu },
      title = { Fully Understanding Generic Objects: Modeling, Segmentation, and Reconstruction },
      booktitle = { In Proceeding of IEEE Computer Vision and Pattern Recognition },
      address = { Nashville, TN },
      month = { June },
      year = { 2021 },
    }

2020

  • Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence
    Feng Liu, Xiaoming Liu
    In Proceeding of 2020 Conference on Neural Information Processing Systems (NeurIPS 2020), Virtual, Dec. 2020 (Oral)
    Bibtex | PDF | arXiv | Supplemental | Poster | Project Webpage | Code | Video
  • @inproceedings{ learning-implicit-functions-for-topology-varying-dense-3d-shape-correspondence,
      author = { Feng Liu and Xiaoming Liu },
      title = { Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence },
      booktitle = { In Proceeding of 2020 Conference on Neural Information Processing Systems },
      address = { Virtual },
      month = { December },
      year = { 2020 },
    }
  • The Edge of Depth: Explicit Constraints between Segmentation and Depth
    Shengjie Zhu, Garrick Brazil, Xiaoming Liu
    In Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2020), Seattle, WA, Jun. 2020
    Bibtex | PDF | arXiv | Supplemental | Project Webpage | Code | Video
  • @inproceedings{ the-edge-of-depth-explicit-constraints-between-segmentation-and-depth,
      author = { Shengjie Zhu and Garrick Brazil and Xiaoming Liu },
      title = { The Edge of Depth: Explicit Constraints between Segmentation and Depth },
      booktitle = { In Proceeding of IEEE Computer Vision and Pattern Recognition },
      address = { Seattle, WA },
      month = { June },
      year = { 2020 },
    }

2019

  • Recurrent Flow-Guided Semantic Forecasting
    Adam M. Terwilliger, Garrick Brazil, Xiaoming Liu
    Proc. IEEE Winter Conference on Application of Computer Vision (WACV 2019), Hawaii, Jan. 2019
    Bibtex | PDF | arXiv | Poster | Project Webpage | Code
  • @inproceedings{ recurrent-flow-guided-semantic-forecasting,
      author = { Adam M. Terwilliger and Garrick Brazil and Xiaoming Liu },
      title = { Recurrent Flow-Guided Semantic Forecasting },
      booktitle = { Proc. IEEE Winter Conference on Application of Computer Vision },
      address = { Hawaii },
      month = { January },
      year = { 2019 },
    }

2017

  • Joint Multi-Leaf Segmentation, Alignment, and Tracking from Fluorescence Plant Videos
    Xi Yin, Xiaoming Liu, Jin Chen, David M. Kramer
    IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 40, No. 6, pp.1411-1423, , Jul. 2017
    Bibtex | PDF | arXiv | Project Webpage | Code
  • @article{ joint-multi-leaf-segmentation-alignment-and-tracking-from-fluorescence-plant-videos,
      author = { Xi Yin and Xiaoming Liu and Jin Chen and David M. Kramer },
      title = { Joint Multi-Leaf Segmentation, Alignment, and Tracking from Fluorescence Plant Videos },
      journal = { IEEE Transactions on Pattern Analysis and Machine Intelligence },
      volume = { 40 },
      number = { 6 },
      month = { July },
      year = { 2017 },
      pages = { 1411--1423 },
    }
  • Illuminating Pedestrians via Simultaneous Detection & Segmentation
    Garrick Brazil, Xi Yin, Xiaoming Liu
    In Proceeding of International Conference on Computer Vision (ICCV 2017), Venice, Italy, Oct. 2017
    Bibtex | PDF | arXiv | Poster | Project Webpage | Code
  • @inproceedings{ illuminating-pedestrians-via-simultaneous-detection-segmentation,
      author = { Garrick Brazil and Xi Yin and Xiaoming Liu },
      title = { Illuminating Pedestrians via Simultaneous Detection & Segmentation },
      booktitle = { In Proceeding of International Conference on Computer Vision },
      address = { Venice, Italy },
      month = { October },
      year = { 2017 },
    }

2016

2014

  • Image Segmentation of Mesenchymal Stem Cells in Diverse Culturing Conditions
    Muhammad Jamal Afridi, Chun Liu, Christina Chan, Seungik Baek, Xiaoming Liu
    Proc. IEEE Winter Conference on Application of Computer Vision (WACV 2014), Steamboat Springs, USA, Mar. 2014
    Bibtex | PDF | Project Webpage
  • @inproceedings{ image-segmentation-of-mesenchymal-stem-cells-in-diverse-culturing-conditions,
      author = { Muhammad Jamal Afridi and Chun Liu and Christina Chan and Seungik Baek and Xiaoming Liu },
      title = { Image Segmentation of Mesenchymal Stem Cells in Diverse Culturing Conditions },
      booktitle = { Proc. IEEE Winter Conference on Application of Computer Vision },
      address = { Steamboat Springs, USA },
      month = { March },
      year = { 2014 },
    }

2011

  • Automatic Surveillance Video Matting Using a Shape Prior
    Ting Yu, Xiaoming Liu, Ser-Nam Lim, Nils Krahnstoever, Peter Tu
    Proc. 11th IEEE Workshop on Visual Surveillance (ICCV 2011), Barcelona, Spain, Sep. 2011
    Bibtex | PDF
  • @inproceedings{ automatic-surveillance-video-matting-using-a-shape-prior,
      author = { Ting Yu and Xiaoming Liu and Ser-Nam Lim and Nils Krahnstoever and Peter Tu },
      title = { Automatic Surveillance Video Matting Using a Shape Prior },
      booktitle = { Proc. 11th IEEE Workshop on Visual Surveillance },
      address = { Barcelona, Spain },
      month = { September },
      year = { 2011 },
    }