The h5-index is the h-index for articles published in the last 5 complete years. According to Google Scholar Metrics, the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) is ranked 4th in the h5-index rankings.
In 2022, UCF’s Center for Research in Computer Vision (CRCV) had 9 papers accepted into the CVPR conference, covering topics like Anomaly Detection, Few-Shot Learning, Capsule Networks, and more.
If these topics sound like something you are interested in researching, or maybe you have some ideas of your own, check out the CRCV’s new Master’s Degree in Computer Vision by visiting https://www.crcv.ucf.edu/mscv/
You can access the CRCV Publications Page for enhanced search capabilities.
2022
Ristea, Nicolae-Catalin; Madan, Neelu; Ionescu, Radu Tudor; Nasrollahi, Kamal; Khan, Fahad Shahbaz; Moeslund, Thomas B.; Shah, Mubarak
Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection Conference
IEEE Computer Vision and Pattern Recognition, 2022.
BibTeX | Links:
@conference{nokey,
title = {Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection},
author = {Nicolae-Catalin Ristea and Neelu Madan and Radu Tudor Ionescu and Kamal Nasrollahi and Fahad Shahbaz Khan and Thomas B. Moeslund and Mubarak Shah},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/SSPCAB_camera-arxiv.pdf},
year = {2022},
date = {2022-06-19},
urldate = {2022-06-19},
publisher = {IEEE Computer Vision and Pattern Recognition},
keywords = {},
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.
BibTeX | Links:
@conference{nokey,
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 = {},
pubstate = {published},
tppubtype = {conference}
}
Acsintoae, Andra; Florescu, Andrei; Georgescu, Mariana-Iuliana; Mare, Tudor; Sumedrea, Paul; Ionescu, Radu Tudor; Khan, Fahad Shahbaz; Shah, Mubarak
UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection Conference
IEEE Computer Vision and Pattern Recognition, 2022.
BibTeX | Links:
@conference{nokey,
title = {UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection},
author = {Andra Acsintoae and Andrei Florescu and Mariana-Iuliana Georgescu and Tudor Mare and Paul Sumedrea and Radu Tudor Ionescu and Fahad Shahbaz Khan and Mubarak Shah},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/04315.pdf
https://github.com/lilygeorgescu/UBnormal},
year = {2022},
date = {2022-06-19},
urldate = {2022-06-19},
publisher = {IEEE Computer Vision and Pattern Recognition},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Dave, Ishan Rajendrakumar; Chen, Chen; Shah, Mubarak
SPAct: Self-supervised Privacy Preservation for Action Recognition Conference
IEEE Computer Vision and Pattern Recognition, 2022.
BibTeX | Links:
@conference{nokey,
title = {SPAct: Self-supervised Privacy Preservation for Action Recognition},
author = {Ishan Rajendrakumar Dave and Chen Chen and Mubarak Shah},
url = {https://arxiv.org/pdf/2203.15205.pdf
https://github.com/DAVEISHAN/SPAct},
year = {2022},
date = {2022-06-19},
urldate = {2022-06-19},
publisher = {IEEE Computer Vision and Pattern Recognition},
keywords = {},
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.
BibTeX | Links:
@conference{nokey,
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 = {},
pubstate = {published},
tppubtype = {conference}
}
Mendieta, Matias; Yang, Taojiannan; Wang, Pu; Lee, Minwoo; Ding, Zhengming; Chen, Chen
Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning Conference
IEEE Computer Vision and Pattern Recognition, 2022.
BibTeX | Links:
@conference{nokey,
title = {Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning},
author = {Matias Mendieta and Taojiannan Yang and Pu Wang and Minwoo Lee and Zhengming Ding and Chen Chen},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/11405.pdf
https://www.crcv.ucf.edu/wp-content/uploads/2018/11/11405_supp.pdf},
year = {2022},
date = {2022-06-19},
urldate = {2022-06-19},
publisher = {IEEE Computer Vision and Pattern Recognition},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Zhu, Sijie; Shah, Mubarak; Chen, Chen
TransGeo: Transformer Is All You Need for Cross-view Image Geo-localization Conference
IEEE Computer Vision and Pattern Recognition, 2022.
BibTeX | Links:
@conference{nokey,
title = {TransGeo: Transformer Is All You Need for Cross-view Image Geo-localization},
author = {Sijie Zhu and Mubarak Shah and Chen Chen},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/11695.pdf
https://www.crcv.ucf.edu/wp-content/uploads/2018/11/11695-supp.pdf},
year = {2022},
date = {2022-06-19},
urldate = {2022-06-19},
publisher = {IEEE Computer Vision and Pattern Recognition},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Cao, Jiale; Pang, Yenwai; Anwer, Rao Muhammad; Cholakkal, Hisham; Xie, Jin; Shah, Mubarak; Khan, Fahad Shahbaz
PSTR: End-to-End One-Step Person Search With Transformers Conference
IEEE Computer Vision and Pattern Recognition, 2022.
@conference{nokey,
title = {PSTR: End-to-End One-Step Person Search With Transformers},
author = {Jiale Cao and Yenwai Pang and Rao Muhammad Anwer and Hisham Cholakkal and Jin Xie and Mubarak Shah and Fahad Shahbaz Khan},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/05237-2.pdf
https://github.com/JialeCao001/PSTR},
year = {2022},
date = {2022-06-19},
urldate = {2022-06-19},
publisher = {IEEE Computer Vision and Pattern Recognition},
abstract = {We propose a novel one-step transformer-based person search framework, PSTR, that jointly performs person detection and re-identification (re-id) in a single architecture. PSTR comprises a person search-specialized (PSS) module that contains a detection encoder-decoder for person detection along with a discriminative re-id decoder for person re-id. The discriminative re-id decoder utilizes a multi-level supervision scheme with a shared decoder for
discriminative re-id feature learning and also comprises a part attention block to encode relationship between different parts of a person. We further introduce a simple multi-scale scheme to support re-id across person instances at different scales. PSTR jointly achieves the diverse objectives of object-level recognition (detection) and instance-level matching (re-id). To the best of our knowledge, we are the first to propose an end-to-end one-step
transformer-based person search framework. Experiments are performed on two popular benchmarks: CUHK-SYSU and PRW. Our extensive ablations reveal the merits of the proposed contributions. Further, the proposed PSTR sets a new state-of-the-art on both benchmarks. On the challenging
PRW benchmark, PSTR achieves a mean average precision (mAP) score of 56.5%. The source code is available at https://github.com/JialeCao001/PSTR.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
discriminative re-id feature learning and also comprises a part attention block to encode relationship between different parts of a person. We further introduce a simple multi-scale scheme to support re-id across person instances at different scales. PSTR jointly achieves the diverse objectives of object-level recognition (detection) and instance-level matching (re-id). To the best of our knowledge, we are the first to propose an end-to-end one-step
transformer-based person search framework. Experiments are performed on two popular benchmarks: CUHK-SYSU and PRW. Our extensive ablations reveal the merits of the proposed contributions. Further, the proposed PSTR sets a new state-of-the-art on both benchmarks. On the challenging
PRW benchmark, PSTR achieves a mean average precision (mAP) score of 56.5%. The source code is available at https://github.com/JialeCao001/PSTR.
Gupta, Akshita; Narayan, Sanath; Joseph, K J; Khan, Salman; Khan, Fahad Shahbaz; Shah, Mubarak
OW-DETR: Open-world Detection Transformer Conference
IEEE Computer Vision and Pattern Recognition, 2022.
@conference{nokey,
title = {OW-DETR: Open-world Detection Transformer},
author = {Akshita Gupta and Sanath Narayan and K J Joseph and Salman Khan and Fahad Shahbaz Khan and Mubarak Shah},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/03815.pdf
https://www.crcv.ucf.edu/wp-content/uploads/2018/11/03815-supp.pdf
https://github.com/akshitac8/OW-DETR.},
year = {2022},
date = {2022-06-19},
urldate = {2022-06-19},
publisher = {IEEE Computer Vision and Pattern Recognition},
abstract = {Open-world object detection (OWOD) is a challenging computer vision problem, where the task is to detect a known set of object categories while simultaneously identifying unknown objects. Additionally, the model must incrementally learn new classes that become known in the next training episodes. Distinct from standard object detection, the OWOD setting poses significant challenges for generating quality candidate proposals on potentially unknown objects, separating the unknown objects from the background and detecting diverse unknown objects. Here, we introduce a novel end-to-end transformer-based framework, OW-DETR, for open-world object detection. The proposed OW-DETR comprises three dedicated components
namely, attention-driven pseudo-labeling, novelty classification and objectness scoring to explicitly address the aforementioned OWOD challenges. Our OW-DETR explicitly encodes multi-scale contextual information, possesses less inductive bias, enables knowledge transfer from known classes to the unknown class and can better discriminate between unknown objects and background. Comprehensive experiments are performed on two benchmarks: MS-COCO and PASCAL VOC. The extensive ablations reveal the merits of our proposed contributions. Further, our model outperforms the recently introduced OWOD approach, ORE, with absolute gains ranging from 1.8% to 3.3% in terms of unknown recall on MS-COCO. In the case of incremental
object detection, OW-DETR outperforms the state-of-the art for all settings on PASCAL VOC. Our code is available at https://github.com/akshitac8/OW-DETR.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
namely, attention-driven pseudo-labeling, novelty classification and objectness scoring to explicitly address the aforementioned OWOD challenges. Our OW-DETR explicitly encodes multi-scale contextual information, possesses less inductive bias, enables knowledge transfer from known classes to the unknown class and can better discriminate between unknown objects and background. Comprehensive experiments are performed on two benchmarks: MS-COCO and PASCAL VOC. The extensive ablations reveal the merits of our proposed contributions. Further, our model outperforms the recently introduced OWOD approach, ORE, with absolute gains ranging from 1.8% to 3.3% in terms of unknown recall on MS-COCO. In the case of incremental
object detection, OW-DETR outperforms the state-of-the art for all settings on PASCAL VOC. Our code is available at https://github.com/akshitac8/OW-DETR.