2022
Karim, Nazmul; Rizve, Mamshad Nayeem; Rahnavard, Nazanin; Mian, Ajmal; Shah, Mubarak
UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning Conference
IEEE Computer Vision and Pattern Recognition, 2022.
Tags: Contrastive Learning, CVPR, Noisy Labels, Semi-supervised learning | Links:
@conference{nokey_102,
title = {UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning},
author = {Nazmul Karim and Mamshad Nayeem Rizve and Nazanin Rahnavard and Ajmal Mian and Mubarak Shah},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/07363.pdf
https://www.crcv.ucf.edu/wp-content/uploads/2018/11/07363-supp.pdf
https://github.com/nazmul-karim170/unicon-noisy-label},
year = {2022},
date = {2022-06-19},
urldate = {2022-06-19},
publisher = {IEEE Computer Vision and Pattern Recognition},
keywords = {Contrastive Learning, CVPR, Noisy Labels, Semi-supervised learning},
pubstate = {published},
tppubtype = {conference}
}
Kumar, Akash; Rawat, Yogesh Singh
End-to-End Semi-Supervised Learning for Video Action Detection Conference
IEEE Computer Vision and Pattern Recognition, 2022.
Tags: CVPR, Semi-supervised learning | Links:
@conference{nokey_105,
title = {End-to-End Semi-Supervised Learning for Video Action Detection},
author = {Akash Kumar and Yogesh Singh Rawat},
url = {https://arxiv.org/pdf/2203.04251.pdf},
year = {2022},
date = {2022-06-19},
urldate = {2022-06-19},
publisher = {IEEE Computer Vision and Pattern Recognition},
keywords = {CVPR, Semi-supervised learning},
pubstate = {published},
tppubtype = {conference}
}
Karim, Nazmul; Rizve, Mamshad Nayeem; Rahnavard, Nazanin; Mian, Ajmal; Shah, Mubarak
UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning Conference
IEEE Computer Vision and Pattern Recognition, 2022.
Tags: Contrastive Learning, CVPR, Noisy Labels, Semi-supervised learning | Links:
@conference{nokey_150,
title = {UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning},
author = {Nazmul Karim and Mamshad Nayeem Rizve and Nazanin Rahnavard and Ajmal Mian and Mubarak Shah},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/07363.pdf
https://www.crcv.ucf.edu/wp-content/uploads/2018/11/07363-supp.pdf
https://github.com/nazmul-karim170/unicon-noisy-label},
year = {2022},
date = {2022-06-19},
urldate = {2022-06-19},
publisher = {IEEE Computer Vision and Pattern Recognition},
keywords = {Contrastive Learning, CVPR, Noisy Labels, Semi-supervised learning},
pubstate = {published},
tppubtype = {conference}
}
Kumar, Akash; Rawat, Yogesh Singh
End-to-End Semi-Supervised Learning for Video Action Detection Conference
IEEE Computer Vision and Pattern Recognition, 2022.
Tags: CVPR, Semi-supervised learning | Links:
@conference{nokey_153,
title = {End-to-End Semi-Supervised Learning for Video Action Detection},
author = {Akash Kumar and Yogesh Singh Rawat},
url = {https://arxiv.org/pdf/2203.04251.pdf},
year = {2022},
date = {2022-06-19},
urldate = {2022-06-19},
publisher = {IEEE Computer Vision and Pattern Recognition},
keywords = {CVPR, Semi-supervised learning},
pubstate = {published},
tppubtype = {conference}
}
2021
Rizve, Mamshad Nayeem; Duarte, Kevin; Rawat, Yogesh Singh; Shah, Mubarak
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning Conference
Ninth International Conference on Learning Representations (ICLR), 2021.
Tags: ICLR, Network Calibration, Pseudo-Labeling, Semi-supervised learning | Links:
@conference{Rizve2021,
title = {In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning},
author = {Mamshad Nayeem Rizve and Kevin Duarte and Yogesh Singh Rawat and Mubarak Shah},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/In-Defense-Of-Pseudo-Labeling.pdf
https://github.com/nayeemrizve/ups
https://www.crcv.ucf.edu/wp-content/uploads/2018/11/UPS_ICLR2021_Slides.pdf
https://www.crcv.ucf.edu/wp-content/uploads/2018/11/UPS_ICLR2021_Slides.pptx
https://www.crcv.ucf.edu/wp-content/uploads/2018/11/UPS_Poster_ICLR2021.png},
year = {2021},
date = {2021-05-04},
urldate = {2021-05-04},
booktitle = {Ninth International Conference on Learning Representations (ICLR)},
keywords = {ICLR, Network Calibration, Pseudo-Labeling, Semi-supervised learning},
pubstate = {published},
tppubtype = {conference}
}
Rizve, Mamshad Nayeem; Duarte, Kevin; Rawat, Yogesh Singh; Shah, Mubarak
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning Conference
Ninth International Conference on Learning Representations (ICLR), 2021.
Tags: ICLR, Network Calibration, Pseudo-Labeling, Semi-supervised learning | Links:
@conference{Rizve2021d,
title = {In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning},
author = {Mamshad Nayeem Rizve and Kevin Duarte and Yogesh Singh Rawat and Mubarak Shah},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/In-Defense-Of-Pseudo-Labeling.pdf
https://github.com/nayeemrizve/ups
https://www.crcv.ucf.edu/wp-content/uploads/2018/11/UPS_ICLR2021_Slides.pdf
https://www.crcv.ucf.edu/wp-content/uploads/2018/11/UPS_ICLR2021_Slides.pptx
https://www.crcv.ucf.edu/wp-content/uploads/2018/11/UPS_Poster_ICLR2021.png},
year = {2021},
date = {2021-05-04},
urldate = {2021-05-04},
booktitle = {Ninth International Conference on Learning Representations (ICLR)},
keywords = {ICLR, Network Calibration, Pseudo-Labeling, Semi-supervised learning},
pubstate = {published},
tppubtype = {conference}
}
0000
Test Bachelor Thesis
0000.
Tags: Crowd Analysis, Damage Assessment, DIVA, Drone Video Analysis, EEG/Brain, Incremental Learning, Learning with Less Labels, LIDAR, Multi-Modal Learning, Noisy Labels, Open World, Out of Distribution (OOD), Privacy Preservation, Pseudo-Labeling, SAR, Self-Joint Learning, Semi-supervised learning, Tiny Actions, Video Synopsis, Video Synthesis, View-Invariance, Weakly Supervised Learning, Wildlife Preservation
@bachelorthesis{nokey_135,
title = {Test},
keywords = {Crowd Analysis, Damage Assessment, DIVA, Drone Video Analysis, EEG/Brain, Incremental Learning, Learning with Less Labels, LIDAR, Multi-Modal Learning, Noisy Labels, Open World, Out of Distribution (OOD), Privacy Preservation, Pseudo-Labeling, SAR, Self-Joint Learning, Semi-supervised learning, Tiny Actions, Video Synopsis, Video Synthesis, View-Invariance, Weakly Supervised Learning, Wildlife Preservation},
pubstate = {published},
tppubtype = {bachelorthesis}
}
Test Bachelor Thesis
0000.
Tags: Crowd Analysis, Damage Assessment, DIVA, Drone Video Analysis, EEG/Brain, Incremental Learning, Learning with Less Labels, LIDAR, Multi-Modal Learning, Noisy Labels, Open World, Out of Distribution (OOD), Privacy Preservation, Pseudo-Labeling, SAR, Self-Joint Learning, Semi-supervised learning, Tiny Actions, Video Synopsis, Video Synthesis, View-Invariance, Weakly Supervised Learning, Wildlife Preservation
@bachelorthesis{nokey_171,
title = {Test},
keywords = {Crowd Analysis, Damage Assessment, DIVA, Drone Video Analysis, EEG/Brain, Incremental Learning, Learning with Less Labels, LIDAR, Multi-Modal Learning, Noisy Labels, Open World, Out of Distribution (OOD), Privacy Preservation, Pseudo-Labeling, SAR, Self-Joint Learning, Semi-supervised learning, Tiny Actions, Video Synopsis, Video Synthesis, View-Invariance, Weakly Supervised Learning, Wildlife Preservation},
pubstate = {published},
tppubtype = {bachelorthesis}
}