1. 

   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. 

   Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence

   Feng Liu, Xiaoming Liu

   The goal of this paper is to learn dense 3D shape correspondence for topology-varying objects in an unsupervised manner. Conventional implicit functions estimate the occupancy of a 3D point given a shape latent code. Instead, our novel implicit function produces a part embedding vector for each 3D point, which is ...

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   Keywords: 3D Shape Correspondence, Semantic Segmentation

3. 

   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

4. 

   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

5. 

   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

6. 

   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

• Learning Implicit Functions for Dense 3D Shape Correspondence of Generic Objects
  Feng Liu, Xiaoming Liu
  IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), , Nov. 2022 (in press)
  Bibtex | PDF | arXiv
@article{ learning-implicit-functions-for-dense-3d-shape-correspondence-of-generic-objects,
  author = { Feng Liu and Xiaoming Liu },
  title = { Learning Implicit Functions for Dense 3D Shape Correspondence of Generic Objects },
  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

• Leaf Segmentation in Plant Phenotyping: A Collation Study
  Hanno Scharr, Massimo Minervini, Andrew P. French, Christian Klukas, David M. Kramer, Xiaoming Liu, Imanol L. Muntion, Jean-Michel Pape, Gerrit Polder, Danijela Vukadinovic, Xi Yin, Sotirios A. Tsaftaris
  Machine Vision and Application, Vol. [u'27', u'7'], No. 4, pp.585-606, , May. 2016
  Bibtex | PDF | Project Webpage | Code
@article{ leaf-segmentation-in-plant-phenotyping-a-collation-study,
  author = { Hanno Scharr and Massimo Minervini and Andrew P. French and Christian Klukas and David M. Kramer and Xiaoming Liu and Imanol L. Muntion and Jean-Michel Pape and Gerrit Polder and Danijela Vukadinovic and Xi Yin and Sotirios A. Tsaftaris },
  title = { Leaf Segmentation in Plant Phenotyping: A Collation Study },
  journal = { Machine Vision and Application },
  volume = { [u'27', u'7'] },
  number = { 4 },
  month = { May },
  year = { 2016 },
  pages = { 585--606 },
}

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 },
}