Shahab Oveis Gharan - IEEE Xplore Author Profile

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Author details

Shahab Oveis Gharan

Also published under: S. Oveis Gharan

Publications

16

Citations

119

Publications by Year

20072024

Co-Authors:

Ramin BabaeeAlireza BayestehMartin BouchardHamid FarmanbarShervan Fashandi

Show All Co-Authors (11)

Shahab Oveis Gharan

Also published under: S. Oveis Gharan

Affiliation

Ciena Corp.

Ottawa

ON, Canada

Publication Topics

Adaptive Equalization,
Amplified Spontaneous Emission,
Analog-to-digital Converter,
Bit Error,
Bit Error Rate,
Channel Coherence Time,
Channel Equalization,
Channel Estimation,
Channel Matrix,
Channel Response,
Circuit Elements,
Codeword

Biography

Shahab Oveis Gharan (S'02) was born in Isfahan, Iran, in 1980. He received the B. Sc. and M. sc. degrees in electrical engineering from Sharif University of Technology (SUT), Tehran, Iran, in 2002 and 2005, respectively, and the Ph.D. degree from University of Waterloo, Waterloo, ON, Canada, in 2010. He is currently working with the Ciena Corporation. His research interests are fiberoptic communication, cooperative wireless communications, and interference networks.(Based on document published on 21 March 2013).

Publications

16

Citations

119

Publications by Year

20072024

Co-Authors:

Ramin Babaee
Alireza Bayesteh
Martin Bouchard
Hamid Farmanbar
Shervan Fashandi

Show All Co-Authors (11)

Author's Published Works

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Showing 1-15 of 15 results

Conferences (11)

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School of Automation, Northwestern Polytechnical University, Xi’an, China(3)
Amazon Web Service(2)
School of Data Science, Fudan University(2)
Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China(2)
2023 IEEE/CVF International Conference on Computer Vision (ICCV)(3)
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(2)
IEEE Transactions on Pattern Analysis and Machine Intelligence(2)
2017 IEEE International Conference on Computer Vision (ICCV)(1)
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)(1)
IEEE(15)
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Content and Salient Semantics Collaboration for Cloth-Changing Person Re-Identification

Qizao Wang;Xuelin Qian;Bin Li;Lifeng Chen;Yanwei Fu;Xiangyang Xue

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2025 | Conference Paper |

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Cloth-changing person re-identification aims at recognizing the same person with clothing changes across non-overlapping cameras. Advanced methods either resort to identity-related auxiliary modalities (e.g., sketches, silhouettes, and keypoints) or clothing labels to mitigate the impact of clothes. However, relying on unpractical and inflexible auxiliary modalities or annotations limits their rea...Show More

Content and Salient Semantics Collaboration for Cloth-Changing Person Re-Identification

Qizao Wang;Xuelin Qian;Bin Li;Lifeng Chen;Yanwei Fu;Xiangyang Xue

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2025 | Conference Paper |

Hierarchical Context Interaction and Reasoning with Transformer for Emotion Recognition

Wenxuan Wang;Chenglei Wang;Xiaomei Wang;Xuli Shen;Qing Xu;Xuelin Qian

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2025 | Conference Paper |

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Emotion recognition is an important task in computer vision. However, current approaches using hard associations (e.g., element-wise addition or concatenation) suffer from information pollution. To overcome these challenges and utilize information at different scales, we present a novel Transformer-based emotion recognition (TransEmo) framework utilizing only the RGB modality as input. TransEmo is...Show More

Hierarchical Context Interaction and Reasoning with Transformer for Emotion Recognition

Wenxuan Wang;Chenglei Wang;Xiaomei Wang;Xuli Shen;Qing Xu;Xuelin Qian

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2025 | Conference Paper |

Dynamic Routing and Knowledge Re-Learning for Data-Free Black-Box Attack

Xuelin Qian;Wenxuan Wang;Yu-Gang Jiang;Xiangyang Xue;Yanwei Fu

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2025 | Volume: 47, Issue: 1 | Journal Article |

Cited by: Papers (1)

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Deep learning models have emerged as strong and efficient tools that can be applied to a broad spectrum of complex learning problems and many real-world applications. However, more and more works show that deep models are vulnerable to adversarial examples. Compared to vanilla attack settings, this paper advocates a more practical setting of data-free black-box attack, for which the attackers can ...Show More

Dynamic Routing and Knowledge Re-Learning for Data-Free Black-Box Attack

Xuelin Qian;Wenxuan Wang;Yu-Gang Jiang;Xiangyang Xue;Yanwei Fu

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2025 | Volume: 47, Issue: 1 | Journal Article |

Exploring Fine-Grained Representation and Recomposition for Cloth-Changing Person Re-Identification

Qizao Wang;Xuelin Qian;Bin Li;Xiangyang Xue;Yanwei Fu

IEEE Transactions on Information Forensics and Security

Year: 2024 | Volume: 19 | Journal Article |

Cited by: Papers (5)

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Cloth-changing person Re-IDentification (Re-ID) is a particularly challenging task, suffering from two limitations of inferior discriminative features and limited training samples. Existing methods mainly leverage auxiliary information to facilitate identity-relevant feature learning, including soft-biometrics features of shapes or gaits, and additional labels of clothing. However, this informatio...Show More

Exploring Fine-Grained Representation and Recomposition for Cloth-Changing Person Re-Identification

Qizao Wang;Xuelin Qian;Bin Li;Xiangyang Xue;Yanwei Fu

IEEE Transactions on Information Forensics and Security

Year: 2024 | Volume: 19 | Journal Article |

Rethinking Amodal Video Segmentation from Learning Supervised Signals with Object-centric Representation

Ke Fan;Jingshi Lei;Xuelin Qian;Miaopeng Yu;Tianjun Xiao;Tong He;Zheng Zhang;Yanwei Fu

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

Cited by: Papers (2)

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Video amodal segmentation is a particularly challenging task in computer vision, which requires to deduce the full shape of an object from the visible parts of it. Recently, some studies have achieved promising performance by using motion flow to integrate information across frames under a self-supervised setting. However, motion flow has a clear limitation by the two factors of moving cameras and...Show More

Rethinking Amodal Video Segmentation from Learning Supervised Signals with Object-centric Representation

Ke Fan;Jingshi Lei;Xuelin Qian;Miaopeng Yu;Tianjun Xiao;Tong He;Zheng Zhang;Yanwei Fu

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

Coarse-to-Fine Amodal Segmentation with Shape Prior

Jianxiong Gao;Xuelin Qian;Yikai Wang;Tianjun Xiao;Tong He;Zheng Zhang;Yanwei Fu

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

Cited by: Papers (7)

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Amodal object segmentation is a challenging task that involves segmenting both visible and occluded parts of an object. In this paper, we propose a novel approach, called Coarse-to-Fine Segmentation (C2F-Seg), that addresses this problem by progressively modeling the amodal segmentation. C2F-Seg initially reduces the learning space from the pixel-level image space to the vector-quantized latent sp...Show More

Coarse-to-Fine Amodal Segmentation with Shape Prior

Jianxiong Gao;Xuelin Qian;Yikai Wang;Tianjun Xiao;Tong He;Zheng Zhang;Yanwei Fu

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

Learning Versatile 3D Shape Generation with Improved Auto-regressive Models

Simian Luo;Xuelin Qian;Yanwei Fu;Yinda Zhang;Ying Tai;Zhenyu Zhang;Chengjie Wang;Xiangyang Xue

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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Auto-Regressive (AR) models have achieved impressive results in 2D image generation by modeling joint distributions in the grid space. While this approach has been extended to the 3D domain for powerful shape generation, it still has two limitations: expensive computations on volumetric grids and ambiguous auto-regressive order along grid dimensions. To overcome these limitations, we propose the I...Show More

Learning Versatile 3D Shape Generation with Improved Auto-regressive Models

Simian Luo;Xuelin Qian;Yanwei Fu;Yinda Zhang;Ying Tai;Zhenyu Zhang;Chengjie Wang;Xiangyang Xue

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

Causally-Aware Intraoperative Imputation for Overall Survival Time Prediction

Xiang Li;Xuelin Qian;Litian Liang;Lingjie Kong;Qiaole Dong;Jiejun Chen;Dingxia Liu;Xiuzhong Yao;Yanwei Fu

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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Previous efforts in vision community are mostly made on learning good representations from visual patterns. Beyond this, this paper emphasizes the high-level ability of causal reasoning. We thus present a case study of solving the challenging task of Overall Survival (OS) time in primary liver cancers. Critically, the prediction of OS time at the early stage remains challenging, due to the unobvio...Show More

Causally-Aware Intraoperative Imputation for Overall Survival Time Prediction

Xiang Li;Xuelin Qian;Litian Liang;Lingjie Kong;Qiaole Dong;Jiejun Chen;Dingxia Liu;Xiuzhong Yao;Yanwei Fu

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

ImpDet: Exploring Implicit Fields for 3D Object Detection

Xuelin Qian;Li Wang;Yi Zhu;Li Zhang;Yanwei Fu;Xiangyang Xue

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

Year: 2023 | Conference Paper |

Cited by: Papers (4)

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Conventional 3D object detection approaches concentrate on bounding boxes representation learning with several parameters, i.e., localization, dimension, and orientation. Despite its popularity and universality, such a straightforward paradigm is sensitive to slight numerical deviations, especially in localization. By exploiting the property that point clouds are naturally captured on the surface ...Show More

ImpDet: Exploring Implicit Fields for 3D Object Detection

Xuelin Qian;Li Wang;Yi Zhu;Li Zhang;Yanwei Fu;Xiangyang Xue

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

Year: 2023 | Conference Paper |

DST: Dynamic Substitute Training for Data-free Black-box Attack

Wenxuan Wang;Xuelin Qian;Yanwei Fu;Xiangyang Xue

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2022 | Conference Paper |

Cited by: Papers (14)

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With the wide applications of deep neural network models in various computer vision tasks, more and more works study the model vulnerability to adversarial examples. For data-free black box attack scenario, existing methods are inspired by the knowledge distillation, and thus usually train a substitute model to learn knowledge from the target model using generated data as input. However, the subst...Show More

DST: Dynamic Substitute Training for Data-free Black-box Attack

Wenxuan Wang;Xuelin Qian;Yanwei Fu;Xiangyang Xue

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2022 | Conference Paper |

M$^3$Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening From CT Imaging

Xuelin Qian;Huazhu Fu;Weiya Shi;Tao Chen;Yanwei Fu;Fei Shan;Xiangyang Xue

IEEE Journal of Biomedical and Health Informatics

Year: 2020 | Volume: 24, Issue: 12 | Journal Article |

Cited by: Papers (47)

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To counter the outbreak of COVID-19, the accurate diagnosis of suspected cases plays a crucial role in timely quarantine, medical treatment, and preventing the spread of the pandemic. Considering the limited training cases and resources (e.g, time and budget), we propose a Multi-task Multi-slice Deep Learning System (M$^3$Lung-Sys) for multi-class lung pneumonia screening from CT imaging, which on...Show More

M$^3$Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening From CT Imaging

Xuelin Qian;Huazhu Fu;Weiya Shi;Tao Chen;Yanwei Fu;Fei Shan;Xiangyang Xue

IEEE Journal of Biomedical and Health Informatics

Year: 2020 | Volume: 24, Issue: 12 | Journal Article |

FM2u-Net: Face Morphological Multi-Branch Network for Makeup-Invariant Face Verification

Wenxuan Wang;Yanwei Fu;Xuelin Qian;Yu-Gang Jiang;Qi Tian;Xiangyang Xue

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2020 | Conference Paper |

Cited by: Papers (10)

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It is challenging in learning a makeup-invariant face verification model, due to (1) insufficient makeup/non-makeup face training pairs, (2) the lack of diverse makeup faces, and (3) the significant appearance changes caused by cosmetics. To address these challenges, we propose a unified Face Morphological Multi-branch Network (FMMu-Net) for makeup-invariant face verification, which can simultaneo...Show More

FM2u-Net: Face Morphological Multi-Branch Network for Makeup-Invariant Face Verification

Wenxuan Wang;Yanwei Fu;Xuelin Qian;Yu-Gang Jiang;Qi Tian;Xiangyang Xue

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2020 | Conference Paper |

When Person Re-identification Meets Changing Clothes

Fangbin Wan;Yang Wu;Xuelin Qian;Yixiong Chen;Yanwei Fu

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2020 | Conference Paper |

Cited by: Papers (82)

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Person re-identification (ReID) is now an active research topic for AI-based video surveillance applications such as specific person search, but the practical issue that the target person(s) may change clothes (clothes inconsistency problem) has been overlooked for long. For the first time, this paper systematically studies this problem. We first overcome the difficulty of lack of suitable dataset...Show More

When Person Re-identification Meets Changing Clothes

Fangbin Wan;Yang Wu;Xuelin Qian;Yixiong Chen;Yanwei Fu

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2020 | Conference Paper |

Leader-Based Multi-Scale Attention Deep Architecture for Person Re-Identification

Xuelin Qian;Yanwei Fu;Tao Xiang;Yu-Gang Jiang;Xiangyang Xue

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2020 | Volume: 42, Issue: 2 | Journal Article |

Cited by: Papers (81)

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Person re-identification (re-id) aims to match people across non-overlapping camera views in a public space. This is a challenging problem because the people captured in surveillance videos often wear similar clothing. Consequently, the differences in their appearance are typically subtle and only detectable at particular locations and scales. In this paper, we propose a deep re-id network (MuDeep...Show More

Leader-Based Multi-Scale Attention Deep Architecture for Person Re-Identification

Xuelin Qian;Yanwei Fu;Tao Xiang;Yu-Gang Jiang;Xiangyang Xue

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2020 | Volume: 42, Issue: 2 | Journal Article |

Multi-scale Deep Learning Architectures for Person Re-identification

Xuelin Qian;Yanwei Fu;Yu-Gang Jiang;Tao Xiang;Xiangyang Xue

2017 IEEE International Conference on Computer Vision (ICCV)

Year: 2017 | Conference Paper |

Cited by: Papers (188)

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Person Re-identification (re-id) aims to match people across non-overlapping camera views in a public space. It is a challenging problem because many people captured in surveillance videos wear similar clothes. Consequently, the differences in their appearance are often subtle and only detectable at the right location and scales. Existing re-id models, particularly the recently proposed deep learn...Show More

Multi-scale Deep Learning Architectures for Person Re-identification

Xuelin Qian;Yanwei Fu;Yu-Gang Jiang;Tao Xiang;Xiangyang Xue

2017 IEEE International Conference on Computer Vision (ICCV)

Year: 2017 | Conference Paper |

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