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Wanhua Li - IEEE Xplore Author Profile
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Wanhua Li

Publications
15
Citations
265
Publications by Year
20192024
Co-Authors:
Show All Co-Authors (29)
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Wanhua Li

Affiliation

Department of Surveying and Mapping and Space Environment
Space Engineering University
Beijing, China

Publication Topics

Biography

Wanhua Li received the B.E. degree in surveying and mapping engineering from Inner Mongolia University of Science and Technology, Baotou, China, in 2017, and the M.E. degree in surveying and mapping from Guizhou University, Guizhou, China, in 2021. She is currently pursuing the Ph.D. degree with the Information Institute, Space Engineering University, Beijing, China.,Her research interests include computer graphics, computer vision, image processing, and deep learning.(Based on document published on 23 July 2024).
Publications
15
Citations
265
Publications by Year
20192024
Co-Authors:
Show All Co-Authors (29)
Author's Published Works

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EDR-TransUnet: Integrating Enhanced Dual Relation-Attention With Transformer U-Net for Multiscale Rock Segmentation on Mars

Yutong Jia;Gang Wan;Wanhua Li;Chu Li;Jia Liu;Dianwei Cong;Lei Liu

IEEE Transactions on Geoscience and Remote Sensing
Year: 2024 | Volume: 62 | Journal Article |
Detecting Mars rocks on celestial bodies is crucial for the obstacle avoidance and path planning of space probes in deep space environments. However, the irregular shapes, background similarity, sparse pixel features, and susceptibility to lighting and dust effects make rock detection methods face significant problems in complex deep space environments. This challenge significantly impacts the acc...Show More

EDR-TransUnet: Integrating Enhanced Dual Relation-Attention With Transformer U-Net for Multiscale Rock Segmentation on Mars

Yutong Jia;Gang Wan;Wanhua Li;Chu Li;Jia Liu;Dianwei Cong;Lei Liu

Year: 2024 | Volume: 62 | Journal Article |

SD-NeRF: Towards Lifelike Talking Head Animation via Spatially-Adaptive Dual-Driven NeRFs

Shuai Shen;Wanhua Li;Xiaoke Huang;Zheng Zhu;Jie Zhou;Jiwen Lu

IEEE Transactions on Multimedia
Year: 2024 | Volume: 26 | Journal Article |
Cited by: Papers (5)
Recent years have witnessed great progress in audio-driven talking head animation. Among these methods, the 3D-based ones better preserve the 3D consistency of the generated head and produce more natural results compared with 2D-based approaches. However, most 3D-based methods employ 3D morphable face models as the intermediate representation and involve multi-stage training, which may lead to err...Show More

SD-NeRF: Towards Lifelike Talking Head Animation via Spatially-Adaptive Dual-Driven NeRFs

Shuai Shen;Wanhua Li;Xiaoke Huang;Zheng Zhu;Jie Zhou;Jiwen Lu

Year: 2024 | Volume: 26 | Journal Article |

CLIP-Cluster: CLIP-Guided Attribute Hallucination for Face Clustering

Shuai Shen;Wanhua Li;Xiaobing Wang;Dafeng Zhang;Zhezhu Jin;Jie Zhou;Jiwen Lu

2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
One of the most important yet rarely studied challenges for supervised face clustering is the large intra-class variance caused by different face attributes such as age, pose, and expression. Images of the same identity but with different face attributes usually tend to be clustered into different sub-clusters. For the first time, we proposed an attribute hallucination framework named CLIP-Cluster...Show More

CLIP-Cluster: CLIP-Guided Attribute Hallucination for Face Clustering

Shuai Shen;Wanhua Li;Xiaobing Wang;Dafeng Zhang;Zhezhu Jin;Jie Zhou;Jiwen Lu

Year: 2023 | Conference Paper |

DiffTalk: Crafting Diffusion Models for Generalized Audio-Driven Portraits Animation

Shuai Shen;Wenliang Zhao;Zibin Meng;Wanhua Li;Zheng Zhu;Jie Zhou;Jiwen Lu

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (40)
Talking head synthesis is a promising approach for the video production industry. Recently, a lot of effort has been devoted in this research area to improve the generation quality or enhance the model generalization. However, there are few works able to address both issues simultaneously, which is essential for practical applications. To this end, in this paper, we turn attention to the emerging ...Show More

DiffTalk: Crafting Diffusion Models for Generalized Audio-Driven Portraits Animation

Shuai Shen;Wenliang Zhao;Zibin Meng;Wanhua Li;Zheng Zhu;Jie Zhou;Jiwen Lu

Year: 2023 | Conference Paper |

STAR-FC: Structure-Aware Face Clustering on Ultra-Large-Scale Graphs

Shuai Shen;Wanhua Li;Zheng Zhu;Jie Zhou;Jiwen Lu

IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2023 | Volume: 45, Issue: 11 | Journal Article |
Cited by: Papers (3)
Face clustering is a promising method for annotating unlabeled face images. Recent supervised approaches have boosted the face clustering accuracy greatly, however their performance is still far from satisfactory. These methods can be roughly divided into global-based and local-based ones. Global-based methods suffer from the limitation of training data scale, while local-based ones are inefficien...Show More

STAR-FC: Structure-Aware Face Clustering on Ultra-Large-Scale Graphs

Shuai Shen;Wanhua Li;Zheng Zhu;Jie Zhou;Jiwen Lu

Year: 2023 | Volume: 45, Issue: 11 | Journal Article |

Discrepancy-Aware Meta-Learning for Zero-Shot Face Manipulation Detection

Bingyao Yu;Xiu Li;Wanhua Li;Jie Zhou;Jiwen Lu

IEEE Transactions on Image Processing
Year: 2023 | Volume: 32 | Journal Article |
Cited by: Papers (4)
In this paper, we propose a discrepancy-aware meta-learning approach for zero-shot face manipulation detection, which aims to learn a discriminative model maximizing the generalization to unseen face manipulation attacks with the guidance of the discrepancy map. Unlike existing face manipulation detection methods that usually present algorithmic solutions to the known face manipulation attacks, wh...Show More

Discrepancy-Aware Meta-Learning for Zero-Shot Face Manipulation Detection

Bingyao Yu;Xiu Li;Wanhua Li;Jie Zhou;Jiwen Lu

Year: 2023 | Volume: 32 | Journal Article |

Learning Adaptive Patch Generators for Mask-Robust Image Inpainting

Hongyi Sun;Wanhua Li;Yueqi Duan;Jie Zhou;Jiwen Lu

IEEE Transactions on Multimedia
Year: 2023 | Volume: 25 | Journal Article |
Cited by: Papers (8)
In this paper, we propose a Mask-Robust Inpainting Network (MRIN) approach to recover the masked areas of an image. Most existing methods learn a single model for image inpainting, under a basic assumption that all masks are from the same type. However, we discover that the masks are usually complex and exhibit various shapes and sizes at different locations of an image, where a single model canno...Show More

Learning Adaptive Patch Generators for Mask-Robust Image Inpainting

Hongyi Sun;Wanhua Li;Yueqi Duan;Jie Zhou;Jiwen Lu

Year: 2023 | Volume: 25 | Journal Article |

MetaAge: Meta-Learning Personalized Age Estimators

Wanhua Li;Jiwen Lu;Abudukelimu Wuerkaixi;Jianjiang Feng;Jie Zhou

IEEE Transactions on Image Processing
Year: 2022 | Volume: 31 | Journal Article |
Cited by: Papers (6)
Different people age in different ways. Learning a personalized age estimator for each person is a promising direction for age estimation given that it better models the personalization of aging processes. However, most existing personalized methods suffer from the lack of large-scale datasets due to the high-level requirements: identity labels and enough samples for each person to form a long-ter...Show More

MetaAge: Meta-Learning Personalized Age Estimators

Wanhua Li;Jiwen Lu;Abudukelimu Wuerkaixi;Jianjiang Feng;Jie Zhou

Year: 2022 | Volume: 31 | Journal Article |

Frequency-Aware Spatiotemporal Transformers for Video Inpainting Detection

Bingyao Yu;Wanhua Li;Xiu Li;Jiwen Lu;Jie Zhou

2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2021 | Conference Paper |
Cited by: Papers (18)
In this paper, we propose a Frequency-Aware Spatiotemporal Transformer (FAST) for video inpainting detection, which aims to simultaneously mine the traces of video in-painting from spatial, temporal, and frequency domains. Unlike existing deep video inpainting detection methods that usually rely on hand-designed attention modules and memory mechanism, our proposed FAST have innate global self-atte...Show More

Frequency-Aware Spatiotemporal Transformers for Video Inpainting Detection

Bingyao Yu;Wanhua Li;Xiu Li;Jiwen Lu;Jie Zhou

Year: 2021 | Conference Paper |

Meta-Mining Discriminative Samples for Kinship Verification

Wanhua Li;Shiwei Wang;Jiwen Lu;Jianjiang Feng;Jie Zhou

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2021 | Conference Paper |
Cited by: Papers (16)
Kinship verification aims to find out whether there is a kin relation for a given pair of facial images. Kinship verification databases are born with unbalanced data. For a database with N positive kinship pairs, we naturally obtain N(N − 1) negative pairs. How to fully utilize the limited positive pairs and mine discriminative information from sufficient negative samples for kinship verification ...Show More

Meta-Mining Discriminative Samples for Kinship Verification

Wanhua Li;Shiwei Wang;Jiwen Lu;Jianjiang Feng;Jie Zhou

Year: 2021 | Conference Paper |

Structure-Aware Face Clustering on a Large-Scale Graph with 107 Nodes

Shuai Shen;Wanhua Li;Zheng Zhu;Guan Huang;Dalong Du;Jiwen Lu;Jie Zhou

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2021 | Conference Paper |
Cited by: Papers (20)
Face clustering is a promising method for annotating un-labeled face images. Recent supervised approaches have boosted the face clustering accuracy greatly, however their performance is still far from satisfactory. These methods can be roughly divided into global-based and local-based ones. Global-based methods suffer from the limitation of training data scale, while local-based ones are difficult...Show More

Structure-Aware Face Clustering on a Large-Scale Graph with 107 Nodes

Shuai Shen;Wanhua Li;Zheng Zhu;Guan Huang;Dalong Du;Jiwen Lu;Jie Zhou

Year: 2021 | Conference Paper |

Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression

Wanhua Li;Xiaoke Huang;Jiwen Lu;Jianjiang Feng;Jie Zhou

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2021 | Conference Paper |
Cited by: Papers (27)
Uncertainty is the only certainty there is. Modeling data uncertainty is essential for regression, especially in unconstrained settings. Traditionally the direct regression formulation is considered and the uncertainty is modeled by modifying the output space to a certain family of probabilistic distributions. On the other hand, classification based regression and ranking based solutions are more ...Show More

Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression

Wanhua Li;Xiaoke Huang;Jiwen Lu;Jianjiang Feng;Jie Zhou

Year: 2021 | Conference Paper |

Reasoning Graph Networks for Kinship Verification: From Star-Shaped to Hierarchical

Wanhua Li;Jiwen Lu;Abudukelimu Wuerkaixi;Jianjiang Feng;Jie Zhou

IEEE Transactions on Image Processing
Year: 2021 | Volume: 30 | Journal Article |
Cited by: Papers (14)
In this paper, we investigate the problem of facial kinship verification by learning hierarchical reasoning graph networks. Conventional methods usually focus on learning discriminative features for each facial image of a paired sample and neglect how to fuse the obtained two facial image features and reason about the relations between them. To address this, we propose a Star-shaped Reasoning Grap...Show More

Reasoning Graph Networks for Kinship Verification: From Star-Shaped to Hierarchical

Wanhua Li;Jiwen Lu;Abudukelimu Wuerkaixi;Jianjiang Feng;Jie Zhou

Year: 2021 | Volume: 30 | Journal Article |

Graph-Based Kinship Reasoning Network

Wanhua Li;Yingqiang Zhang;Kangchen Lv;Jiwen Lu;Jianjiang Feng;Jie Zhou

2020 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2020 | Conference Paper |
Cited by: Papers (20)
In this paper, we propose a graph-based kinship reasoning (GKR) network for kinship verification, which aims to effectively perform relational reasoning on the extracted features of an image pair. Unlike most existing methods which mainly focus on how to learn discriminative features, our method considers how to compare and fuse the extracted feature pair to reason about the kin relations. The pro...Show More

Graph-Based Kinship Reasoning Network

Wanhua Li;Yingqiang Zhang;Kangchen Lv;Jiwen Lu;Jianjiang Feng;Jie Zhou

Year: 2020 | Conference Paper |

BridgeNet: A Continuity-Aware Probabilistic Network for Age Estimation

Wanhua Li;Jiwen Lu;Jianjiang Feng;Chunjing Xu;Jie Zhou;Qi Tian

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2019 | Conference Paper |
Cited by: Papers (82)
Age estimation is an important yet very challenging problem in computer vision. Existing methods for age estimation usually apply a divide-and-conquer strategy to deal with heterogeneous data caused by the non-stationary aging process. However, the facial aging process is also a continuous process, and the continuity relationship between different components has not been effectively exploited. In ...Show More

BridgeNet: A Continuity-Aware Probabilistic Network for Age Estimation

Wanhua Li;Jiwen Lu;Jianjiang Feng;Chunjing Xu;Jie Zhou;Qi Tian

Year: 2019 | Conference Paper |

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