Projects

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    GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection

    Abhinav Kumar, Garrick Brazil, Xiaoming Liu

    Modern 3D object detectors have immensely benefited from the end-to-end learning idea. However, most of them use a post-processing algorithm called Non-Maximal Suppression (NMS) only during inference. While there were attempts to include NMS in the training pipeline for tasks such as 2D object detection, they have been less widely ...

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    Keywords: 3D Object Detection

Publications

2021

  • GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection
    Abhinav Kumar, Garrick Brazil, 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{ groomed-nms-grouped-mathematically-differentiable-nms-for-monocular-3d-object-detection,
      author = { Abhinav Kumar and Garrick Brazil and Xiaoming Liu },
      title = { GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection },
      booktitle = { In Proceeding of IEEE Computer Vision and Pattern Recognition },
      address = { Nashville, TN },
      month = { June },
      year = { 2021 },
    }

2020

  • LUVLi Face Alignment: Estimating Landmarks’ Location, Uncertainty, and Visibility Likelihood
    Abhinav Kumar*, Tim K. Marks*, Wenxuan Mou*, Ye Wang, Michael Jones, Anoop Cherian, Toshiaki Koike-Akino, Xiaoming Liu, Chen Feng
    In Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2020), Seattle, WA, Jun. 2020
    Bibtex | PDF | arXiv | Supplemental | Code | Video | Dataset
  • @inproceedings{ luvli-face-alignment-estimating-landmarks-location-uncertainty-and-visibility-likelihood,
      author = { Abhinav Kumar* and Tim K. Marks* and Wenxuan Mou* and Ye Wang and Michael Jones and Anoop Cherian and Toshiaki Koike-Akino and Xiaoming Liu and Chen Feng },
      title = { LUVLi Face Alignment: Estimating Landmarks’ Location, Uncertainty, and Visibility Likelihood },
      booktitle = { In Proceeding of IEEE Computer Vision and Pattern Recognition },
      address = { Seattle, WA },
      month = { June },
      year = { 2020 },
    }

2019

  • UGLLI Face Alignment:Estimating Uncertainty with Gaussian Log-Likelihood Loss
    Abhinav Kumar*, Tim K.Marks*, Wenxuan Mou*, Chen Feng, Xiaoming Liu
    In Proceeding of International Conference on Computer Vision Workshops (ICCVW 2019), Seoul, Korea, Oct. 2019 (Best Oral Presentation)
    Bibtex | PDF | Poster
  • @inproceedings{ uglli-face-alignmentestimating-uncertainty-with-gaussian-log-likelihood-loss,
      author = { Abhinav Kumar* and Tim K.Marks* and Wenxuan Mou* and Chen Feng and Xiaoming Liu },
      title = { UGLLI Face Alignment:Estimating Uncertainty with Gaussian Log-Likelihood Loss },
      booktitle = { In Proceeding of International Conference on Computer Vision Workshops },
      address = { Seoul, Korea },
      month = { October },
      year = { 2019 },
    }