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Pengxu Wei - IEEE Xplore Author Profile
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Pengxu Wei

Publications
37
Citations
598
Publications by Year
20152024
Co-Authors:
Show All Co-Authors (368)
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Pengxu Wei

Affiliation

School of Computer Science and Engineering
Sun Yat-sen University
Guangzhou, China
Peng Cheng Laboratory, China

Publication Topics

Biography

Pengxu Wei received the B.S. degree in computer science and technology from China University of Mining and Technology, Beijing, China, in 2011, and the Ph.D. degree from the School of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences, Beijing, in 2018.,She is currently a Research Scientist with Sun Yat-sen University, Guangzhou, China. Her current research interests include computer vision and machine learning, specifically for data-driven vision and scene image recognition.(Based on document published on 14 September 2021).
Publications
37
Citations
598
Publications by Year
20152024
Co-Authors:
Show All Co-Authors (368)
Author's Published Works

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SAM-COD+: SAM-guided Unified Framework for Weakly-Supervised Camouflaged Object Detection

Huafeng Chen;Pengxu Wei;Guangqian Guo;Shan Gao

IEEE Transactions on Circuits and Systems for Video Technology
Year: 2024 | Early Access Article |
Most Camouflaged Object Detection (COD) methods heavily rely on mask annotations, which are time-consuming and labor-intensive to acquire. Existing weakly-supervised COD approaches exhibit significantly inferior performance compared to fully-supervised methods and struggle to simultaneously support all the existing types of camouflaged object labels, including scribbles, bounding boxes, and points...Show More

SAM-COD+: SAM-guided Unified Framework for Weakly-Supervised Camouflaged Object Detection

Huafeng Chen;Pengxu Wei;Guangqian Guo;Shan Gao

Year: 2024 | Early Access Article |

Towards Real-world Continuous Super-Resolution: Benchmark and Method

Xingbei Guo;Ziping Ma;Qing Wang;Pengxu Wei

2024 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2024 | Conference Paper |
Continuous Super-Resolution (CSR) has garnered considerable popularity for its capability to reconstruct high-resolution (HR) images from low-resolution (LR) inputs at various scales, thereby holding significant practical value in real-world applications. However, the existing studies have relied solely on synthetic datasets due to the scarcity of real-world continuous datasets. In this paper, we ...Show More

Towards Real-world Continuous Super-Resolution: Benchmark and Method

Xingbei Guo;Ziping Ma;Qing Wang;Pengxu Wei

Year: 2024 | Conference Paper |

NTIRE 2024 Challenge on Image Super-Resolution (×4): Methods and Results

Zheng Chen;Zongwei Wu;Eduard Zamfir;Kai Zhang;Yulun Zhang;Radu Timofte;Xiaokang Yang;Hongyuan Yu;Cheng Wan;Yuxin Hong;Zhijuan Huang;Yajun Zou;Yuan Huang;Jiamin Lin;Bingnan Han;Xianyu Guan;Yongsheng Yu;Daoan Zhang;Xuanwu Yin;Kunlong Zuo;Jinhua Hao;Kai Zhao;Kun Yuan;Ming Sun;Chao Zhou;Hongyu An;Xinfeng Zhang;Zhiyuan Song;Ziyue Dong;Qing Zhao;Xiaogang Xu;Pengxu Wei;Zhi-chao Dou;Gui-ling Wang;Chih-Chung Hsu;Chia-Ming Lee;Yi-Shiuan Chou;Cansu Korkmaz;A. Murat Tekalp;Yubin Wei;Xiaole Yan;Binren Li;Haonan Chen;Siqi Zhang;Sihan Chen;Amogh Joshi;Nikhil Akalwadi;Sampada Malagi;Palani Yashaswini;Chaitra Desai;Ramesh Ashok Tabib;Ujwala Patil;Uma Mudenagudi;Anjali Sarvaiya;Pooja Choksy;Jagrit Joshi;Shubh Kawa;Kishor Upla;Sushrut Patwardhan;Raghavendra Ramachandra;Sadat Hossain;Geongi Park;S. M. Nadim Uddin;Hao Xu;Yanhui Guo;Aman Urumbekov;Xingzhuo Yan;Wei Hao;Minghan Fu;Isaac Orais;Samuel Smith;Ying Liu;Wangwang Jia;Qisheng Xu;Kele Xu;Weijun Yuan;Zhan Li;Wenqin Kuang;Ruijin Guan;Ruting Deng;Zhao Zhang;Bo Wang;Suiyi Zhao;Yan Luo;Yanyan Wei;Asif Hussain Khan;Christian Micheloni;Niki Martinel

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2024 | Conference Paper |
Cited by: Papers (18)
This paper reviews the NTIRE 2024 challenge on image super-resolution (×4), highlighting the solutions proposed and the outcomes obtained. The challenge involves generating corresponding high-resolution (HR) images, magnified by a factor of four, from low-resolution (LR) inputs using prior information. The LR images originate from bicubic downsampling degradation. The aim of the challenge is to ob...Show More

NTIRE 2024 Challenge on Image Super-Resolution (×4): Methods and Results

Zheng Chen;Zongwei Wu;Eduard Zamfir;Kai Zhang;Yulun Zhang;Radu Timofte;Xiaokang Yang;Hongyuan Yu;Cheng Wan;Yuxin Hong;Zhijuan Huang;Yajun Zou;Yuan Huang;Jiamin Lin;Bingnan Han;Xianyu Guan;Yongsheng Yu;Daoan Zhang;Xuanwu Yin;Kunlong Zuo;Jinhua Hao;Kai Zhao;Kun Yuan;Ming Sun;Chao Zhou;Hongyu An;Xinfeng Zhang;Zhiyuan Song;Ziyue Dong;Qing Zhao;Xiaogang Xu;Pengxu Wei;Zhi-chao Dou;Gui-ling Wang;Chih-Chung Hsu;Chia-Ming Lee;Yi-Shiuan Chou;Cansu Korkmaz;A. Murat Tekalp;Yubin Wei;Xiaole Yan;Binren Li;Haonan Chen;Siqi Zhang;Sihan Chen;Amogh Joshi;Nikhil Akalwadi;Sampada Malagi;Palani Yashaswini;Chaitra Desai;Ramesh Ashok Tabib;Ujwala Patil;Uma Mudenagudi;Anjali Sarvaiya;Pooja Choksy;Jagrit Joshi;Shubh Kawa;Kishor Upla;Sushrut Patwardhan;Raghavendra Ramachandra;Sadat Hossain;Geongi Park;S. M. Nadim Uddin;Hao Xu;Yanhui Guo;Aman Urumbekov;Xingzhuo Yan;Wei Hao;Minghan Fu;Isaac Orais;Samuel Smith;Ying Liu;Wangwang Jia;Qisheng Xu;Kele Xu;Weijun Yuan;Zhan Li;Wenqin Kuang;Ruijin Guan;Ruting Deng;Zhao Zhang;Bo Wang;Suiyi Zhao;Yan Luo;Yanyan Wei;Asif Hussain Khan;Christian Micheloni;Niki Martinel

Year: 2024 | Conference Paper |

Diff-Mosaic: Augmenting Realistic Representations in Infrared Small Target Detection via Diffusion Prior

Yukai Shi;Yupei Lin;Pengxu Wei;Xiaoyu Xian;Tianshui Chen;Liang Lin

IEEE Transactions on Geoscience and Remote Sensing
Year: 2024 | Volume: 62 | Journal Article |
Cited by: Papers (1)
Recently, researchers have proposed various deep learning methods to accurately detect infrared targets with the characteristics of indistinct shape and texture. Due to the limited variety of infrared datasets, training deep learning models with good generalization poses a challenge. To augment the infrared dataset, researchers employ data augmentation techniques, which often involve generating ne...Show More

Diff-Mosaic: Augmenting Realistic Representations in Infrared Small Target Detection via Diffusion Prior

Yukai Shi;Yupei Lin;Pengxu Wei;Xiaoyu Xian;Tianshui Chen;Liang Lin

Year: 2024 | Volume: 62 | Journal Article |

IDF-CR: Iterative Diffusion Process for Divide-and-Conquer Cloud Removal in Remote-Sensing Images

Meilin Wang;Yexing Song;Pengxu Wei;Xiaoyu Xian;Yukai Shi;Liang Lin

IEEE Transactions on Geoscience and Remote Sensing
Year: 2024 | Volume: 62 | Journal Article |
Cited by: Papers (4)
Deep learning technologies have demonstrated their effectiveness in removing cloud cover from optical remote-sensing images. Convolutional neural networks (CNNs) exert dominance in the cloud removal tasks. However, constrained by the inherent limitations of convolutional operations, CNNs can address only a modest fraction of cloud occlusion. In recent years, diffusion models have achieved state-of...Show More

IDF-CR: Iterative Diffusion Process for Divide-and-Conquer Cloud Removal in Remote-Sensing Images

Meilin Wang;Yexing Song;Pengxu Wei;Xiaoyu Xian;Yukai Shi;Liang Lin

Year: 2024 | Volume: 62 | Journal Article |

CIPL: Counterfactual Interactive Policy Learning to Eliminate Popularity Bias for Online Recommendation

Yongsen Zheng;Jinghui Qin;Pengxu Wei;Ziliang Chen;Liang Lin

IEEE Transactions on Neural Networks and Learning Systems
Year: 2024 | Volume: 35, Issue: 12 | Journal Article |
Popularity bias, as a long-standing problem in recommender systems (RSs), has been fully considered and explored for offline recommendation systems in most existing relevant researches, but very few studies have paid attention to eliminate such bias in online interactive recommendation scenarios. Bias amplification will become increasingly serious over time due to the existence of feedback loop be...Show More

CIPL: Counterfactual Interactive Policy Learning to Eliminate Popularity Bias for Online Recommendation

Yongsen Zheng;Jinghui Qin;Pengxu Wei;Ziliang Chen;Liang Lin

Year: 2024 | Volume: 35, Issue: 12 | Journal Article |

Routing User-Interest Markov Tree for Scalable Personalized Knowledge-Aware Recommendation

Yongsen Zheng;Pengxu Wei;Ziliang Chen;Chengpei Tang;Liang Lin

IEEE Transactions on Neural Networks and Learning Systems
Year: 2024 | Volume: 35, Issue: 10 | Journal Article |
To facilitate more accurate and explainable recommendation, it is crucial to incorporate side information into user-item interactions. Recently, knowledge graph (KG) has attracted much attention in a variety of domains due to its fruitful facts and abundant relations. However, the expanding scale of real-world data graphs poses severe challenges. In general, most existing KG-based algorithms adopt...Show More

Routing User-Interest Markov Tree for Scalable Personalized Knowledge-Aware Recommendation

Yongsen Zheng;Pengxu Wei;Ziliang Chen;Chengpei Tang;Liang Lin

Year: 2024 | Volume: 35, Issue: 10 | Journal Article |

Multi-Person 3D Pose Estimation With Occlusion Reasoning

Xipeng Chen;Junzheng Zhang;Keze Wang;Pengxu Wei;Liang Lin

IEEE Transactions on Multimedia
Year: 2024 | Volume: 26 | Journal Article |
Cited by: Papers (5)
The performance of existing methods for multi-person 3D pose estimation in crowded scenes is still limited, due to the challenge of heavy overlapping among persons. Attempt to address this issue, we propose a progressive inference scheme, i.e., Articulation-aware Knowledge Exploration (AKE), to improve the multi-person 3D pose models on those samples with complex occlusions at the inference stage....Show More

Multi-Person 3D Pose Estimation With Occlusion Reasoning

Xipeng Chen;Junzheng Zhang;Keze Wang;Pengxu Wei;Liang Lin

Year: 2024 | Volume: 26 | Journal Article |

TopoSeg: Topology-Aware Nuclear Instance Segmentation

Hongliang He;Jun Wang;Pengxu Wei;Fan Xu;Xiangyang Ji;Chang Liu;Jie Chen

2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (4)
Nuclear instance segmentation has been critical for pathology image analysis in medical science, e.g., cancer diagnosis. Current methods typically adopt pixel-wise optimization for nuclei boundary exploration, where rich structural information could be lost for subsequent quantitative morphology assessment. To address this issue, we develop a topology-aware segmentation approach, termed TopoSeg, w...Show More

TopoSeg: Topology-Aware Nuclear Instance Segmentation

Hongliang He;Jun Wang;Pengxu Wei;Fan Xu;Xiangyang Ji;Chang Liu;Jie Chen

Year: 2023 | Conference Paper |

Towards Real-World Burst Image Super-Resolution: Benchmark and Method

Pengxu Wei;Yujing Sun;Xingbei Guo;Chang Liu;Guanbin Li;Jie Chen;Xiangyang Ji;Liang Lin

2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (9)
Despite substantial advances, single-image super-resolution (SISR) is always in a dilemma to reconstruct high-quality images with limited information from one input image, especially in realistic scenarios. In this paper, we establish a large-scale real-world burst super-resolution dataset, i.e., RealBSR, to explore the faithful reconstruction of image details from multiple frames. Furthermore, we...Show More

Towards Real-World Burst Image Super-Resolution: Benchmark and Method

Pengxu Wei;Yujing Sun;Xingbei Guo;Chang Liu;Guanbin Li;Jie Chen;Xiangyang Ji;Liang Lin

Year: 2023 | Conference Paper |

Adversarially Robust Source-free Domain Adaptation with Relaxed Adversarial Training

Yao Xiao;Pengxu Wei;Cong Liu;Liang Lin

2023 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2023 | Conference Paper |
Unsupervised Domain Adaptation (UDA) learns a model for an unlabeled target domain, utilizing a labeled source domain. Most existing works on UDA assume the availability of source data and neglect the adversarial robustness of the models, hindering security-sensitive real-world applications. In this paper, we study adversarially robust source-free UDA, aiming to train a robust target model by adap...Show More

Adversarially Robust Source-free Domain Adaptation with Relaxed Adversarial Training

Yao Xiao;Pengxu Wei;Cong Liu;Liang Lin

Year: 2023 | Conference Paper |

Masked Images Are Counterfactual Samples for Robust Fine-Tuning

Yao Xiao;Ziyi Tang;Pengxu Wei;Cong Liu;Liang Lin

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (6)
Deep learning models are challenged by the distribution shift between the training data and test data. Recently, the large models pre-trained on diverse data have demonstrated unprecedented robustness to various distribution shifts. However, fine-tuning these models can lead to a trade-off between in-distribution (ID) performance and out-of-distribution (OOD) robustness. Existing methods for tackl...Show More

Masked Images Are Counterfactual Samples for Robust Fine-Tuning

Yao Xiao;Ziyi Tang;Pengxu Wei;Cong Liu;Liang Lin

Year: 2023 | Conference Paper |

Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation

Zesen Cheng;Pengchong Qiao;Kehan Li;Siheng Li;Pengxu Wei;Xiangyang Ji;Li Yuan;Chang Liu;Jie Chen

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (26)
Weakly supervised semantic segmentation is typically inspired by class activation maps, which serve as pseudo masks with class-discriminative regions highlighted. Although tremendous efforts have been made to recall precise and complete locations for each class, existing methods still commonly suffer from the unsolicited Out-of-Candidate (OC) error predictions that do not belong to the label candi...Show More

Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation

Zesen Cheng;Pengchong Qiao;Kehan Li;Siheng Li;Pengxu Wei;Xiangyang Ji;Li Yuan;Chang Liu;Jie Chen

Year: 2023 | Conference Paper |

Identity-Preserving Talking Face Generation with Landmark and Appearance Priors

Weizhi Zhong;Chaowei Fang;Yinqi Cai;Pengxu Wei;Gangming Zhao;Liang Lin;Guanbin Li

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (26)
Generating talking face videos from audio attracts lots of research interest. A few person-specific methods can generate vivid videos but require the target speaker's videos for training or fine-tuning. Existing person-generic methods have difficulty in generating realistic and lip-synced videos while preserving identity information. To tackle this problem, we propose a two-stage framework consist...Show More

Identity-Preserving Talking Face Generation with Landmark and Appearance Priors

Weizhi Zhong;Chaowei Fang;Yinqi Cai;Pengxu Wei;Gangming Zhao;Liang Lin;Guanbin Li

Year: 2023 | Conference Paper |

Learning Task-Aligned Mask Query for Instance Segmentation

Bin Fu;Hongliang He;Pengxu Wei;Jie Chen

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2023 | Conference Paper |
Recently, query-based instance segmentation methods have achieved comparable performance to previous state-of-the-art methods. However, the query lacks the learning of the consistency between classification and segmentation tasks, which may lead to misalignment between classification score and mask quality (i.e., mask IoU) and can not result in a reliable ranking for predictions. In this work, we ...Show More

Learning Task-Aligned Mask Query for Instance Segmentation

Bin Fu;Hongliang He;Pengxu Wei;Jie Chen

Year: 2023 | Conference Paper |

Taylor Neural Network for Real-World Image Super-Resolution

Pengxu Wei;Ziwei Xie;Guanbin Li;Liang Lin

IEEE Transactions on Image Processing
Year: 2023 | Volume: 32 | Journal Article |
Cited by: Papers (4)
Due to the difficulty of collecting paired Low-Resolution (LR) and High-Resolution (HR) images, the recent research on single image Super-Resolution (SR) has often been criticized for the data bottleneck of the synthetic image degradation between LRs and HRs. Recently, the emergence of real-world SR datasets, e.g., RealSR and DRealSR, promotes the exploration of Real-World image Super-Resolution (...Show More

Taylor Neural Network for Real-World Image Super-Resolution

Pengxu Wei;Ziwei Xie;Guanbin Li;Liang Lin

Year: 2023 | Volume: 32 | Journal Article |

Real-World Image Super-Resolution by Exclusionary Dual-Learning

Hao Li;Jinghui Qin;Zhijing Yang;Pengxu Wei;Jinshan Pan;Liang Lin;Yukai Shi

IEEE Transactions on Multimedia
Year: 2023 | Volume: 25 | Journal Article |
Cited by: Papers (11)
Real-world image super-resolution is a practical image restoration problem that aims to obtain high-quality images from in-the-wild input, has recently received considerable attention with regard to its tremendous application potentials. Although deep learning-based methods have achieved promising restoration quality on real-world image super-resolution datasets, they ignore the relationship betwe...Show More

Real-World Image Super-Resolution by Exclusionary Dual-Learning

Hao Li;Jinghui Qin;Zhijing Yang;Pengxu Wei;Jinshan Pan;Liang Lin;Yukai Shi

Year: 2023 | Volume: 25 | Journal Article |

Graph-Convolved Factorization Machines for Personalized Recommendation

Yongsen Zheng;Pengxu Wei;Ziliang Chen;Yang Cao;Liang Lin

IEEE Transactions on Knowledge and Data Engineering
Year: 2023 | Volume: 35, Issue: 2 | Journal Article |
Cited by: Papers (12)
Factorization machines (FMs) and their neural network variants (neural FMs) for modeling second-order feature interactions are effective in building modern recommendation systems. However, feature interactions are based upon pairs of features, whereas multi-features correlations commonly arise in real-world financial product recommendation scenarios. We propose an effective neural recommender syst...Show More

Graph-Convolved Factorization Machines for Personalized Recommendation

Yongsen Zheng;Pengxu Wei;Ziliang Chen;Yang Cao;Liang Lin

Year: 2023 | Volume: 35, Issue: 2 | Journal Article |

Dual Adversarial Adaptation for Cross-Device Real-World Image Super-Resolution

Xiaoqian Xu;Pengxu Wei;Weikai Chen;Yang Liu;Mingzhi Mao;Liang Lin;Guanbin Li

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2022 | Conference Paper |
Cited by: Papers (8)
Due to the sophisticated imaging process, an identical scene captured by different cameras could exhibit distinct imaging patterns, introducing distinct proficiency among the super-resolution (SR) models trained on images from different devices. In this paper, we investigate a novel and practical task coded cross-device SR, which strives to adapt a real-world SR model trained on the paired images ...Show More

Dual Adversarial Adaptation for Cross-Device Real-World Image Super-Resolution

Xiaoqian Xu;Pengxu Wei;Weikai Chen;Yang Liu;Mingzhi Mao;Liang Lin;Guanbin Li

Year: 2022 | Conference Paper |

Cross-Domain Action Recognition via Prototypical Graph Alignment

Binbin Yang;Junhao Zhong;Pengxu Wei;Dongyu Zhang;Liang Lin

2022 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2022 | Conference Paper |
Compared with the well-explored cross-domain image recognition, cross-domain action recognition is a more challenging task because not only spatial but also temporal domain gaps exist across domains. Previous works attempt to bridge the temporal domain gap by aligning the domain-related key segments of videos from source and target domains. However, such practice overlooks the heterogeneous tempor...Show More

Cross-Domain Action Recognition via Prototypical Graph Alignment

Binbin Yang;Junhao Zhong;Pengxu Wei;Dongyu Zhang;Liang Lin

Year: 2022 | Conference Paper |

Discovering Implicit Classes Achieves Open Set Domain Adaptation

Jingyu Zhuang;Ziliang Chen;Pengxu Wei;Guanbin Li;Liang Lin

2022 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2022 | Conference Paper |
Cited by: Papers (4)
In Open Set Domain Adaptation (OSDA), large amounts of target samples are drawn from the implicit categories that never appear in the source domain. Due to the lack of their specific belonging, existing methods indiscriminately regard them as a single class “unknown”. We challenge this broadly-adopted practice that may arouse unexpected detrimental ef-fects because the decision boundaries between ...Show More

Discovering Implicit Classes Achieves Open Set Domain Adaptation

Jingyu Zhuang;Ziliang Chen;Pengxu Wei;Guanbin Li;Liang Lin

Year: 2022 | Conference Paper |

NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results

Ren Yang;Radu Timofte;Meisong Zheng;Qunliang Xing;Minglang Qiao;Mai Xu;Lai Jiang;Huaida Liu;Ying Chen;Youcheng Ben;Xiao Zhou;Chen Fu;Pei Cheng;Gang Yu;Junyi Li;Renlong Wu;Zhilu Zhang;Wei Shang;Zhengyao Lv;Yunjin Chen;Mingcai Zhou;Dongwei Ren;Kai Zhang;Wangmeng Zuo;Pavel Ostyakov;Vyal Dmitry;Shakarim Soltanayev;Chervontsev Sergey;Zhussip Magauiya;Xueyi Zou;Youliang Yan;Pablo Navarrete Michelini;Yunhua Lu;Diankai Zhang;Shaoli Liu;Si Gao;Biao Wu;Chengjian Zheng;Xiaofeng Zhang;Kaidi Lu;Ning Wang;Thuong Nguyen Canh;Thong Bach;Qing Wang;Xiaopeng Sun;Haoyu Ma;Shijie Zhao;Junlin Li;Liangbin Xie;Shuwei Shi;Yujiu Yang;Xintao Wang;Jinjin Gu;Chao Dong;Xiaodi Shi;Chunmei Nian;Dong Jiang;Jucai Lin;Zhihuai Xie;Mao Ye;Dengyan Luo;Liuhan Peng;Shengjie Chen;Xin Liu;Qian Wang;Xin Liu;Boyang Liang;Hang Dong;Yuhao Huang;Kai Chen;Xingbei Guo;Yujing Sun;Huilei Wu;Pengxu Wei;Yulin Huang;Junying Chen;Ik Hyun Lee;Sunder Ali Khowaja;Jiseok Yoon

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2022 | Conference Paper |
Cited by: Papers (22)
This paper reviews the NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video. In this challenge, we proposed the LDV 2.0 dataset, which includes the LDV dataset (240 videos) and 95 additional videos. This challenge includes three tracks. Track 1 aims at enhancing the videos compressed by HEVC at a fixed QP. Track 2 and Track 3 target both the super-resolution and qua...Show More

NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results

Ren Yang;Radu Timofte;Meisong Zheng;Qunliang Xing;Minglang Qiao;Mai Xu;Lai Jiang;Huaida Liu;Ying Chen;Youcheng Ben;Xiao Zhou;Chen Fu;Pei Cheng;Gang Yu;Junyi Li;Renlong Wu;Zhilu Zhang;Wei Shang;Zhengyao Lv;Yunjin Chen;Mingcai Zhou;Dongwei Ren;Kai Zhang;Wangmeng Zuo;Pavel Ostyakov;Vyal Dmitry;Shakarim Soltanayev;Chervontsev Sergey;Zhussip Magauiya;Xueyi Zou;Youliang Yan;Pablo Navarrete Michelini;Yunhua Lu;Diankai Zhang;Shaoli Liu;Si Gao;Biao Wu;Chengjian Zheng;Xiaofeng Zhang;Kaidi Lu;Ning Wang;Thuong Nguyen Canh;Thong Bach;Qing Wang;Xiaopeng Sun;Haoyu Ma;Shijie Zhao;Junlin Li;Liangbin Xie;Shuwei Shi;Yujiu Yang;Xintao Wang;Jinjin Gu;Chao Dong;Xiaodi Shi;Chunmei Nian;Dong Jiang;Jucai Lin;Zhihuai Xie;Mao Ye;Dengyan Luo;Liuhan Peng;Shengjie Chen;Xin Liu;Qian Wang;Xin Liu;Boyang Liang;Hang Dong;Yuhao Huang;Kai Chen;Xingbei Guo;Yujing Sun;Huilei Wu;Pengxu Wei;Yulin Huang;Junying Chen;Ik Hyun Lee;Sunder Ali Khowaja;Jiseok Yoon

Year: 2022 | Conference Paper |

CENet: Consolidation-and-Exploration Network for Continuous Domain Adaptation

Chi Zhang;Yalu Cheng;Pengxu Wei;Hongliang He;Jie Chen

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
Unsupervised Domain Adaptation (UDA) deals with transferring knowledge from labeled source domains to an unlabeled target domain under domain shift. However, this does not reflect the breadth of scenarios that arise in real-world applications since source domains could increase. A plausible conjecture is: can we train a life-long learning model learned on continuous source domains given the target...Show More

CENet: Consolidation-and-Exploration Network for Continuous Domain Adaptation

Chi Zhang;Yalu Cheng;Pengxu Wei;Hongliang He;Jie Chen

Year: 2022 | Conference Paper |

CDNet: Centripetal Direction Network for Nuclear Instance Segmentation

Hongliang He;Zhongyi Huang;Yao Ding;Guoli Song;Lin Wang;Qian Ren;Pengxu Wei;Zhiqiang Gao;Jie Chen

2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2021 | Conference Paper |
Cited by: Papers (30)
Nuclear instance segmentation is a challenging task due to a large number of touching and overlapping nuclei in pathological images. Existing methods cannot effectively recognize the accurate boundary owing to neglecting the relationship between pixels (e.g., direction information). In this paper, we propose a novel Centripetal Direction Net-work (CDNet) for nuclear instance segmentation. Specific...Show More

CDNet: Centripetal Direction Network for Nuclear Instance Segmentation

Hongliang He;Zhongyi Huang;Yao Ding;Guoli Song;Lin Wang;Qian Ren;Pengxu Wei;Zhiqiang Gao;Jie Chen

Year: 2021 | Conference Paper |

Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for Open-Set Semi-Supervised Learning

Junkai Huang;Chaowei Fang;Weikai Chen;Zhenhua Chai;Xiaolin Wei;Pengxu Wei;Liang Lin;Guanbin Li

2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2021 | Conference Paper |
Cited by: Papers (28)
Open-set semi-supervised learning (open-set SSL) investigates a challenging but practical scenario where out-of-distribution (OOD) samples are contained in the unlabeled data. While the mainstream technique seeks to completely filter out the OOD samples for semi-supervised learning (SSL), we propose a novel training mechanism that could effectively exploit the presence of OOD data for enhanced fea...Show More

Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for Open-Set Semi-Supervised Learning

Junkai Huang;Chaowei Fang;Weikai Chen;Zhenhua Chai;Xiaolin Wei;Pengxu Wei;Liang Lin;Guanbin Li

Year: 2021 | Conference Paper |

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