Wenhui Li

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Wenhui Li (39)
Anan Liu (33)
Weizhi Nie (15)
Yuting Su (15)
Xuanya Li (14)
Dan Song (11)
Heyu Zhou (5)
Shu Xiang (4)
Song Yang (4)
Zhiqiang Wei (4)
Anh Duc Duong (3)
Minh N Do (3)
Minh Triet Tran (3)
Qiang Li (3)
Tu Khiem Le (3)
Vinh Tiep Nguyen (3)
Xiang Bai (2)
Yijuan Lu (2)
Zan Gao (2)
Zhengya Sun (2)

Publication

Journal/Magazine Names

IEEE Transactions on Multimedia (5)
IEEE Transactions on Circuits and Systems for Video Technology (4)
ACM Transactions on Multimedia Computing, Communications, and Applications (3)
Information Processing and Management: an International Journal (3)
Image and Vision Computing (2)
Journal of Visual Communication and Image Representation (2)
Multimedia Systems (2)
Multimedia Tools and Applications (2)
Computer Methods and Programs in Biomedicine (1)
Computer Vision and Image Understanding (1)
Engineering Applications of Artificial Intelligence (1)
Expert Systems with Applications: An International Journal (1)
International Journal on Semantic Web & Information Systems (1)

Proceedings/Book Names

3DOR '18: Proceedings of the 11th Eurographics Workshop on 3D Object Retrieval (3)
MM '23: Proceedings of the 31st ACM International Conference on Multimedia (3)
IJCAI'18: Proceedings of the 27th International Joint Conference on Artificial Intelligence (2)
IJCAI'20: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (2)
MM '24: Proceedings of the 32nd ACM International Conference on Multimedia (1)

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Other periodicals (25)
Proceedings (11)
Journals (3)

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IEEE Press (9)
Association for Computing Machinery (7)
Pergamon Press, Inc. (5)
Eurographics Association (3)
AAAI Press (2)
Academic Press, Inc. (2)
Butterworth-Heinemann (2)
Kluwer Academic Publishers (2)
Springer-Verlag (2)
Unknown publishers (2)
Elsevier North-Holland, Inc. (1)
Elsevier Science Inc. (1)
IGI Global (1)

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research-article
Adaptive Graph Spatial-Temporal Attention Networks for long lead ENSO prediction
- Chengyu Liang
  Department of Computer Science and Technology, Ocean University of China, Qingdao, Shandong, 266100, China
  ,
- Zhengya Sun
  University of Chinese Academy of Sciences, Beijing, 100190, China
  State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
  ,
- Gaojin Shu
  Department of Computer Science and Technology, Ocean University of China, Qingdao, Shandong, 266100, China
  ,
- Wenhui Li
  School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China
  ,
- An-An Liu
  School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China
  ,
- Zhiqiang Wei
  Department of Computer Science and Technology, Ocean University of China, Qingdao, Shandong, 266100, China
  ,
- Bo Yin
  Department of Computer Science and Technology, Ocean University of China, Qingdao, Shandong, 266100, China
Expert Systems with Applications: An International Journal, Volume 255, Issue PA•Dec 2024 • https://doi.org/10.1016/j.eswa.2024.124492
Abstract
El Niño-Southern Oscillation (ENSO) is a crucial factor in global climate change, which can lead to disasters such as floods, droughts, and heavy rainfall worldwide. Although accurate long-lead prediction of ENSO is essential, the intricate time-...
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Cross-Modal Contrastive Learning with a Style-Mixed Bridge for Single Image 3D Shape Retrieval
- Dan Song
  The School of Electrical and Information Engineering, Tianjin University, Tianjin, China
  ,
- Shumeng Huo
  The School of Electrical and Information Engineering, Tianjin University, Tianjin, China
  ,
- Xinwei Fu
  The School of Electrical and Information Engineering, Tianjin University, Tianjin, China
  ,
- Chumeng Zhang
  The School of Electrical and Information Engineering, Tianjin University, Tianjin, China
  ,
- Wenhui Li
  The School of Electrical and Information Engineering, Tianjin University, Tianjin, China
  ,
- An-An Liu
  The School of Electrical and Information Engineering, Tianjin University, Tianjin, China
ACM Transactions on Multimedia Computing, Communications, and Applications, Volume 20, Issue 12•December 2024, Article No.: 366, pp 1-24 • https://doi.org/10.1145/3689645
Image-based 3D shape retrieval (IBSR) is a cross-modal matching task which searches similar shapes from a 3D repository using a natural image. Continuous attention has been paid to this topic, such as joint embedding, adversarial learning, and contrastive ...
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Multi-fineness Boundaries and the Shifted Ensemble-aware Encoding for Point Cloud Semantic Segmentation
- Ziming Wang
  Jilin University, Changchun, China
  ,
- Boxiang Zhang
  Honor Device Co., Ltd., Shenzhen, China
  ,
- Boxiang Zhang
  Honor Device Co., Ltd., Shenzhen, China
  ,
- Ming Ma
  Jilin University, Changchun, China
  ,
- Ming Ma
  Jilin University, Changchun, China
  ,
- Yue Wang
  Jilin University, Changchun, China
  ,
- Yue Wang
  Jilin University, Changchun, China
  ,
- Taoli Du
  Jilin University, Changchun, China
  ,
- Taoli Du
  Jilin University, Changchun, China
  ,
- Wenhui Li
  Jilin University, Changchun, China
  ,
- Wenhui Li
  Jilin University, Changchun, China
MM '24: Proceedings of the 32nd ACM International Conference on Multimedia•October 2024, pp 1062-1071• https://doi.org/10.1145/3664647.3681341

Point cloud segmentation forms the foundation of 3D scene understanding. Boundaries, the intersections of regions, are prone to mis-segmentation. Current point cloud segmentation models exhibit unsatisfactory performance on boundaries. There is limited ...
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research-article
Dynamic graphs attention for ocean variable forecasting
- Junhao Wang
  Department of Computer Science and Technology, Ocean University of China, Qingdao, Shandong, 266100, China
  ,
- Zhengya Sun
  University of Chinese Academy of Sciences, Beijing, 100190, China
  State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
  ,
- Chunxin Yuan
  Department of Computer Science and Technology, Ocean University of China, Qingdao, Shandong, 266100, China
  ,
- Wenhui Li
  School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China
  ,
- An-An Liu
  School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China
  ,
- Zhiqiang Wei
  Department of Computer Science and Technology, Ocean University of China, Qingdao, Shandong, 266100, China
  ,
- Bo Yin
  Department of Computer Science and Technology, Ocean University of China, Qingdao, Shandong, 266100, China
Engineering Applications of Artificial Intelligence, Volume 133, Issue PC•Jul 2024 • https://doi.org/10.1016/j.engappai.2024.108187
Abstract
Forecasting the ocean dynamics is a critical issue for a wide array of climate extremes and environmental crisis. The dynamic variations are traditionally approached by relying on numerical models with all the related physical processes ...
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Dual-Stage Uncertainty Modeling for Unsupervised Cross-Domain 3D Model Retrieval
- Wenhui Li
  Tianjin University, Tianjin, China
  ,
- Houran Zhou
  Tianjin University, Tianjin, China
  ,
- Chenyu Zhang
  Tianjin University, Tianjin, China
  ,
- Weizhi Nie
  Tianjin University, Tianjin, China
  ,
- Xuanya Li
  Baidu Inc., Beijing, China
  ,
- An-An Liu
  Tianjin University, Tianjin, China
IEEE Transactions on Multimedia, Volume 26•2024, pp 8996-9007 • https://doi.org/10.1109/TMM.2024.3384675
Unsupervised cross-domain 3D model retrieval aims to retrieve unlabeled 3D models (target domain) using labeled 2D images (source domain). Domain adaptation approaches have shown impressive performance for cross-domain 3D model retrieval. However, ...
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Progressive Fourier Adversarial Domain Adaptation for Object Classification and Retrieval
- Tian-Bao Li
  School of Electrical and Information Engineering, Tianjin University, Tianjin, China
  ,
- Yu-Ting Su
  School of Electrical and Information Engineering, Tianjin University, Tianjin, China
  ,
- Dan Song
  School of Electrical and Information Engineering, Tianjin University, Tianjin, China
  ,
- Wen-Hui Li
  School of Electrical and Information Engineering, Tianjin University, Tianjin, China
  ,
- Zhi-Qiang Wei
  School of Information Science and Engineering, Ocean University of China, Qingdao, China
  ,
- An-An Liu
  School of Electrical and Information Engineering, Tianjin University, Tianjin, China
IEEE Transactions on Multimedia, Volume 26•2024, pp 4540-4553 • https://doi.org/10.1109/TMM.2023.3323862
Domain adaptation has been extensively explored as a means of transferring knowledge from the labeled source domain to the unlabeled target domain with disparate data distributions. However, the absence of target annotations and significant domain ...
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Commonsense-Guided Semantic and Relational Consistencies for Image-Text Retrieval
- Wenhui Li
  School of Electrical and Information Engineering, Tianjin University, Tianjin, China
  ,
- Song Yang
  School of Microelectronics, Tianjin University, Tianjin, China
  ,
- Qiang Li
  School of Microelectronics, Tianjin University, Tianjin, China
  ,
- Xuanya Li
  Baidu Inc., Beijing, China
  ,
- An-An Liu
  School of Electrical and Information Engineering, Tianjin University, Tianjin, China
IEEE Transactions on Multimedia, Volume 26•2024, pp 1867-1880 • https://doi.org/10.1109/TMM.2023.3289753
Image-text retrieval, as a fundamental task in the cross-modal field, aims to explore the relationship between visual and textual modalities. Recent methods address this task only by learning the conceptual and syntactical correspondences between cross-...
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Structured serialization semantic transfer network for unsupervised cross-domain recognition and retrieval
- Dan Song
  The School of Electrical and Information Engineering, Tianjin University, Tianjin 300110, China
  The Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China
  ,
- Yuanxiang Yang
  The School of Electrical and Information Engineering, Tianjin University, Tianjin 300110, China
  ,
- Wenhui Li
  The School of Electrical and Information Engineering, Tianjin University, Tianjin 300110, China
  ,
- Xuanya Li
  Baidu Inc., Beijing, China
  ,
- Min Liu
  The College of Electrical and Information Engineering, Hunan University, Hunan 410082, China
  ,
- An-An Liu
  The School of Electrical and Information Engineering, Tianjin University, Tianjin 300110, China
  The Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China
Information Processing and Management: an International Journal, Volume 61, Issue 1•Jan 2024 • https://doi.org/10.1016/j.ipm.2023.103565
Abstract
Unsupervised domain adaptation aims to apply a model trained from labeled datasets to unlabeled datasets, which still exist challenges to minimize the degree of inter-domain sample differences. In the paper, we design the structured serialization ...
Highlights

The spatially structured alignment module establishes intra-domain and inter-domain structural associations among categories.
The category serialization alignment module explores serialization information in the updating process of ...
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Adaptive semantic transfer network for unsupervised 2D image-based 3D model retrieval
- Dan Song
  The School of Electrical and Information Engineering, Tianjin University, Tianjin 300110, China
  The Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China
  ,
- Yuanxiang Yang
  The School of Electrical and Information Engineering, Tianjin University, Tianjin 300110, China
  ,
- Wenhui Li
  The School of Electrical and Information Engineering, Tianjin University, Tianjin 300110, China
  ,
- Zhuang Shao
  Warwick Manufacturing Group, University of Warwick, CV4 7AL, United Kingdom
  ,
- Weizhi Nie
  The School of Electrical and Information Engineering, Tianjin University, Tianjin 300110, China
  ,
- Xuanya Li
  Baidu Inc., Beijing, China
  ,
- An-An Liu
  The School of Electrical and Information Engineering, Tianjin University, Tianjin 300110, China
  The Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China
Computer Vision and Image Understanding, Volume 238, Issue C•Jan 2024 • https://doi.org/10.1016/j.cviu.2023.103858
Abstract
Unsupervised 2D image-based 3D model retrieval has been a highlighted research topic to enable flexible retrieval from 2D photos to 3D shapes. Although what methods we have so far have been great progress in this aspect, it still exists some ...
Highlights

A novel adaptive feature encoding module is proposed to mine diverse information different convolutional layers, which can effectively enhance the cross-domain feature representation and reduce the low-level differences of cross-domain ...
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Focus on Hard Samples: Hierarchical Unbiased Constraints for Cross-Domain 3D Model Retrieval
- Tian-Bao Li
  School of Electrical and Information Engineering, Tianjin University, Tianjin, China
  ,
- An-An Liu
  School of Electrical and Information Engineering, Tianjin University, Tianjin, China
  ,
- Dan Song
  School of Electrical and Information Engineering, Tianjin University, Tianjin, China
  ,
- Wen-Hui Li
  School of Electrical and Information Engineering, Tianjin University, Tianjin, China
  ,
- Xuan-Ya Li
  Baidu Inc., Beijing, China
  ,
- Yu-Ting Su
  School of Electrical and Information Engineering, Tianjin University, Tianjin, China
IEEE Transactions on Circuits and Systems for Video Technology, Volume 33, Issue 11•Nov. 2023, pp 7036-7049 • https://doi.org/10.1109/TCSVT.2023.3266920
Cross-domain 3D model retrieval facilitates the management of explosively emerging unlabeled 3D models with conveniently available 2D images or RGB-D objects, which has attracted more and more attention. The modality gap between query samples (2D images ...
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External Knowledge Dynamic Modeling for Image-text Retrieval
- Song Yang
  Tianjin University & Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Tianjin, China
  ,
- Qiang Li
  Tianjin University, Tianjin, China
  ,
- Wenhui Li
  Tianjin University, Tianjin, China
  ,
- Min Liu
  Hunan University, Hunan, China
  ,
- Xuanya Li
  Baidu, Beijing, China
  ,
- Anan Liu
  Tianjin University & Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Tianjin, China
MM '23: Proceedings of the 31st ACM International Conference on Multimedia•October 2023, pp 5330-5338• https://doi.org/10.1145/3581783.3613786

Image-text retrieval is a fundamental branch in cross-modal retrieval. The core is to explore the semantic correspondence to align relevant image-text pairs. Some existing methods rely on global semantics and co-occurrence frequency to design knowledge ...
1Citation
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Progressive Positive Association Framework for Image and Text Retrieval
- Wenhui Li
  Tianjin University & Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Tianjin, China
  ,
- Yan Wang
  Tianjin University, Tianjin, China
  ,
- Yuting Su
  Tianjin University, Tianjin, China
  ,
- Lanjun Wang
  Tianjin University, Tianjin, China
  ,
- Weizhi Nie
  Tianjin University, Tianjin, China
  ,
- An-An Liu
  Tianjin University & Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Tianjin, China
MM '23: Proceedings of the 31st ACM International Conference on Multimedia•October 2023, pp 4807-4815• https://doi.org/10.1145/3581783.3612507

With the increasing amount of multimedia data, the demand for fast and accurate access to information is growing. Image and text retrieval learns visual and textual semantic relationships for multimedia data management and content recognition. The main ...
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Towards Deconfounded Image-Text Matching with Causal Inference
- Wenhui Li
  Tianjin University & Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Tianjin & Hefei, China
  ,
- Xinqi Su
  Tianjin University, Tianjin, China
  ,
- Dan Song
  Tianjin University, Tianjin, China
  ,
- Lanjun Wang
  Tianjin University, Tianjin, China
  ,
- Kun Zhang
  Meituan, Beijing, China
  ,
- An-An Liu
  Tianjin University & Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Tianjin & Hefei, China
MM '23: Proceedings of the 31st ACM International Conference on Multimedia•October 2023, pp 6264-6273• https://doi.org/10.1145/3581783.3612472

Prior image-text matching methods have shown remarkable performance on many benchmark datasets, but most of them overlook the bias in the dataset, which exists in intra-modal and inter-modal, and tend to learn the spurious correlations that extremely ...
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Dual-Path Rare Content Enhancement Network for Image and Text Matching
- Yan Wang
  School of Electrical and Information Engineering, Tianjin University, Tianjin, China
  ,
- Yuting Su
  School of Electrical and Information Engineering, Tianjin University, Tianjin, China
  ,
- Wenhui Li
  School of Electrical and Information Engineering, Tianjin University, Tianjin, China
  ,
- Jun Xiao
  College of Computer Science, Zhejiang University, Hangzhou, China
  ,
- Xuanya Li
  Baidu Inc., Beijing, China
  ,
- An-An Liu
  School of Electrical and Information Engineering, Tianjin University, Tianjin, China
IEEE Transactions on Circuits and Systems for Video Technology, Volume 33, Issue 10•Oct. 2023, pp 6144-6158 • https://doi.org/10.1109/TCSVT.2023.3254530
Image and text matching plays a crucial role in bridging the cross-modal gap between vision and language, and has achieved great progress due to the deep learning. However, the existing methods still suffer from the long-tail problem, where only a small ...
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Instance-prototype similarity consistency for unsupervised 2D image-based 3D model retrieval
- Wenhui Li
  The School of Electrical and Information Engineering, Tianjin University, China
  ,
- Yuwei Zhang
  Tianjin International Engineering Institute, Tianjin University, China
  ,
- Fan Wang
  Tianjin Shengtong Technology Development Co., Ltd., China
  ,
- Xuanya Li
  Baidu Inc., Beijing, China
  ,
- Yulong Duan
  The 30th Research Institute of China Electronics Technology Corporation, China
  ,
- An-An Liu
  The School of Electrical and Information Engineering, Tianjin University, China
  The Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, China
Information Processing and Management: an International Journal, Volume 60, Issue 4•Jul 2023 • https://doi.org/10.1016/j.ipm.2023.103372
Abstract
The unsupervised 3D model retrieval is designed to joint the information of well-labeled 2D domain and unlabeled 3D domain to learn collaborative representations. Most existing methods adopted semantic alignment, but were inevitably ...
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Multi-loop graph convolutional network for multimodal conversational emotion recognition
- Minjie Ren
  School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
  ,
- Xiangdong Huang
  School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
  ,
- Wenhui Li
  School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
  ,
- Jing Liu
  School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Journal of Visual Communication and Image Representation, Volume 94, Issue C•Jun 2023 • https://doi.org/10.1016/j.jvcir.2023.103846
Abstract
Emotion recognition in conversations (ERC) has gained increasing research attention in recent years due to its wide applications in a surge of emerging tasks, such as social media analysis, dialog generation, and recommender systems. Since ...
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Rare-aware attention network for image–text matching
- Yan Wang
  School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
  Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China
  ,
- Yuting Su
  School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
  ,
- Wenhui Li
  School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
  ,
- Zhengya Sun
  Institute of Automation, Chinese Academy of Sciences, Beijing, China
  ,
- Zhiqiang Wei
  Ocean University of China, Qingdao, Shandong, China
  ,
- Jie Nie
  Ocean University of China, Qingdao, Shandong, China
  ,
- Xuanya Li
  Baidu Inc., Beijing, China
  ,
- An-An Liu
  School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
  Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China
Information Processing and Management: an International Journal, Volume 60, Issue 3•May 2023 • https://doi.org/10.1016/j.ipm.2023.103280
Abstract
Image and text matching bridges visual and textual modality differences and plays a considerable role in cross-modal retrieval. Much progress has been achieved through semantic representation and alignment. However, the distribution of ...
Highlights

Rare-aware attention network is proposed for long-tail on cross-modal matching.
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Prototype-based semantic consistency learning for unsupervised 2D image-based 3D shape retrieval
- An-An Liu
  Tianjin University, 300072, Tianjin, China
  Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, 230088, Hefei, China
  ,
- Yuwei Zhang
  Tianjin University, 300072, Tianjin, China
  ,
- Chenyu Zhang
  Tianjin University, 300072, Tianjin, China
  ,
- Wenhui Li
  Tianjin University, 300072, Tianjin, China
  ,
- Bo Lv
  Baidu Inc., 100085, Beijing, China
  ,
- Lei Lei
  Baidu Inc., 100085, Beijing, China
  ,
- Xuanya Li
  The 30th research institute of China Electronics Technology Group Corporation, 610200, Chengdu, China
Multimedia Systems, Volume 29, Issue 4•Aug 2023, pp 1995-2007 • https://doi.org/10.1007/s00530-023-01086-x
Abstract
In this paper, we study the task of unsupervised 2D image-based 3D shape retrieval (UIBSR), which aims to retrieve unlabeled shapes (target domain) using labeled images (source domain). Previous works on UIBSR mainly focus on aligning the ...
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Semantic Completion and Filtration for Image–Text Retrieval
- Song Yang
  Tianjin University; China and also with the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, China
  ,
- Qiang Li
  Tianjin University, China
  ,
- Wenhui Li
  Tianjin University, China
  ,
- Xuan-Ya Li
  Baidu Inc., Beijing, China
  ,
- Ran Jin
  Zhejiang Wanli University, Ningbo, China
  ,
- Bo Lv
  The 30th Research Institute of China Electronics Technology Group Corporation, ChengDu, China
  ,
- Rui Wang
  The 30th Research Institute of China Electronics Technology Group Corporation, China
  ,
- Anan Liu
  Tianjin University; China and also with the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, China
ACM Transactions on Multimedia Computing, Communications, and Applications, Volume 19, Issue 4•July 2023, Article No.: 140, pp 1-20 • https://doi.org/10.1145/3572844
Image–text retrieval is a vital task in computer vision and has received growing attention, since it connects cross-modality data. It comes with the critical challenges of learning unified representations and eliminating the large gap between visual and ...
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Cross-Domain Image-Object Retrieval Based on Weighted Optimal Transport
- Nian Hu
  School of Electrical and Information Engineering, Tianjin University, Tianjin, China
  ,
- Xiangdong Huang
  School of Electrical and Information Engineering, Tianjin University, Tianjin, China
  ,
- Wenhui Li
  School of Electrical and Information Engineering, Tianjin University, Tianjin, China
  ,
- Xuanya Li
  Baidu Inc., Beijing, China
  ,
- An-An Liu
  School of Electrical and Information Engineering, Tianjin University, Tianjin, China
IEEE Transactions on Multimedia, Volume 25•2023, pp 9557-9571 • https://doi.org/10.1109/TMM.2023.3254889
Given a 2D image query and a pool of 3D objects, the goal of image-object retrieval is to rank the 3D objects according to how well their content fits the query. Previous methods usually project 2D images and 3D objects into a joint embedding space and ...
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Description: The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM bibliographic database, the Guide. Coverage of ACM publications is comprehensive from the 1950's. Coverage of other publishers generally starts in the mid 1980's. The Author Profile Page supplies a quick snapshot of an author's contribution to the field and some rudimentary measures of influence upon it. Over time, the contents of the Author Profile page may expand at the direction of the community.
Please see the following 2007 Turing Award winners' profiles as examples:
History: Disambiguation of author names is of course required for precise identification of all the works, and only those works, by a unique individual. Of equal importance to ACM, author name normalization is also one critical prerequisite to building accurate citation and download statistics. For the past several years, ACM has worked to normalize author names, expand reference capture, and gather detailed usage statistics, all intended to provide the community with a robust set of publication metrics. The Author Profile Pages reveal the first result of these efforts.
Normalization: ACM uses normalization algorithms to weigh several types of evidence for merging and splitting names.
These include:
- co-authors: if we have two names and cannot disambiguate them based on name alone, then we see if they have a co-author in common. If so, this weighs towards the two names being the same person.
- affiliations: names in common with same affiliation weighs toward the two names being the same person.
- publication title: names in common whose works are published in same journal weighs toward the two names being the same person.
- keywords: names in common whose works address the same subject matter as determined from title and keywords, weigh toward being the same person.
The more conservative the merging algorithms, the more bits of evidence are required before a merge is made, resulting in greater precision but lower recall of works for a given Author Profile. Many bibliographic records have only author initials. Many names lack affiliations. With very common family names, typical in Asia, more liberal algorithms result in mistaken merges.

Automatic normalization of author names is not exact. Hence it is clear that manual intervention based on human knowledge is required to perfect algorithmic results. ACM is meeting this challenge, continuing to work to improve the automated merges by tweaking the weighting of the evidence in light of experience.
Bibliometrics: In 1926, Alfred Lotka formulated his power law (known as Lotka's Law) describing the frequency of publication by authors in a given field. According to this bibliometric law of scientific productivity, only a very small percentage (~6%) of authors in a field will produce more than 10 articles while the majority (perhaps 60%) will have but a single article published. With ACM's first cut at author name normalization in place, the distribution of our authors with 1, 2, 3..n publications does not match Lotka's Law precisely, but neither is the distribution curve far off. For a definition of ACM's first set of publication statistics, see Bibliometrics
Future Direction: The initial release of the Author Edit Screen is open to anyone in the community with an ACM account, but it is limited to personal information. An author's photograph, a Home Page URL, and an email may be added, deleted or edited. Changes are reviewed before they are made available on the live site.
ACM will expand this edit facility to accommodate more types of data and facilitate ease of community participation with appropriate safeguards. In particular, authors or members of the community will be able to indicate works in their profile that do not belong there and merge others that do belong but are currently missing.

A direct search interface for Author Profiles will be built.

An institutional view of works emerging from their faculty and researchers will be provided along with a relevant set of metrics.

It is possible, too, that the Author Profile page may evolve to allow interested authors to upload unpublished professional materials to an area available for search and free educational use, but distinct from the ACM Digital Library proper. It is hard to predict what shape such an area for user-generated content may take, but it carries interesting potential for input from the community.

Bibliometrics

The ACM DL is a comprehensive repository of publications from the entire field of computing.

It is ACM's intention to make the derivation of any publication statistics it generates clear to the user.

Average citations per article = The total Citation Count divided by the total Publication Count.
Citation Count = cumulative total number of times all authored works by this author were cited by other works within ACM's bibliographic database. Almost all reference lists in articles published by ACM have been captured. References lists from other publishers are less well-represented in the database. Unresolved references are not included in the Citation Count. The Citation Count is citations TO any type of work, but the references counted are only FROM journal and proceedings articles. Reference lists from books, dissertations, and technical reports have not generally been captured in the database. (Citation Counts for individual works are displayed with the individual record listed on the Author Page.)
Publication Count = all works of any genre within the universe of ACM's bibliographic database of computing literature of which this person was an author. Works where the person has role as editor, advisor, chair, etc. are listed on the page but are not part of the Publication Count.
Publication Years = the span from the earliest year of publication on a work by this author to the most recent year of publication of a work by this author captured within the ACM bibliographic database of computing literature (The ACM Guide to Computing Literature, also known as "the Guide".
Available for download = the total number of works by this author whose full texts may be downloaded from an ACM full-text article server. Downloads from external full-text sources linked to from within the ACM bibliographic space are not counted as 'available for download'.
Average downloads per article = The total number of cumulative downloads divided by the number of articles (including multimedia objects) available for download from ACM's servers.
Downloads (cumulative) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server since the downloads were first counted in May 2003. The counts displayed are updated monthly and are therefore 0-31 days behind the current date. Robotic activity is scrubbed from the download statistics.
Downloads (12 months) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 12-month period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (12-month download counts for individual works are displayed with the individual record.)
Downloads (6 weeks) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 6-week period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (6-week download counts for individual works are displayed with the individual record.)

ACM Author-Izer Service

Summary Description

ACM Author-Izer is a unique service that enables ACM authors to generate and post links on both their homepage and institutional repository for visitors to download the definitive version of their articles from the ACM Digital Library at no charge.

Downloads from these sites are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to definitive version of ACM articles should reduce user confusion over article versioning.

ACM Author-Izer also extends ACM’s reputation as an innovative “Green Path” publisher, making ACM one of the first publishers of scholarly works to offer this model to its authors.

To access ACM Author-Izer, authors need to establish a free ACM web account. Should authors change institutions or sites, they can utilize the new ACM service to disable old links and re-authorize new links for free downloads from a different site.

How ACM Author-Izer Works

Authors may post ACM Author-Izer links in their own bibliographies maintained on their website and their own institution’s repository. The links take visitors to your page directly to the definitive version of individual articles inside the ACM Digital Library to download these articles for free.

The Service can be applied to all the articles you have ever published with ACM.

Depending on your previous activities within the ACM DL, you may need to take up to three steps to use ACM Author-Izer.

For authors who do not have a free ACM Web Account:

Go to the ACM DL http://dl.acm.org/ and click SIGN UP. Once your account is established, proceed to next step.

For authors who have an ACM web account, but have not edited their ACM Author Profile page:

Sign in to your ACM web account and go to your Author Profile page. Click "Add personal information" and add photograph, homepage address, etc. Click ADD AUTHOR INFORMATION to submit change. Once you receive email notification that your changes were accepted, you may utilize ACM Author-izer.

For authors who have an account and have already edited their Profile Page:

Sign in to your ACM web account, go to your Author Profile page in the Digital Library, look for the ACM Author-izer link below each ACM published article, and begin the authorization process. If you have published many ACM articles, you may find a batch Authorization process useful. It is labeled: "Export as: ACM Author-Izer Service"

ACM Author-Izer also provides code snippets for authors to display download and citation statistics for each “authorized” article on their personal pages. Downloads from these pages are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to the definitive version of ACM articles should reduce user confusion over article versioning.

Note: You still retain the right to post your author-prepared preprint versions on your home pages and in your institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library. But any download of your preprint versions will not be counted in ACM usage statistics. If you use these AUTHOR-IZER links instead, usage by visitors to your page will be recorded in the ACM Digital Library and displayed on your page.

FAQ

Q. What is ACM Author-Izer?
A. ACM Author-Izer is a unique, link-based, self-archiving service that enables ACM authors to generate and post links on either their home page or institutional repository for visitors to download the definitive version of their articles for free.
Q. What articles are eligible for ACM Author-Izer?
A. ACM Author-Izer can be applied to all the articles authors have ever published with ACM. It is also available to authors who will have articles published in ACM publications in the future.

Q. Are there any restrictions on authors to use this service?
A. No. An author does not need to subscribe to the ACM Digital Library nor even be a member of ACM.

Q. What are the requirements to use this service?
A. To access ACM Author-Izer, authors need to have a free ACM web account, must have an ACM Author Profile page in the Digital Library, and must take ownership of their Author Profile page.

Q. What is an ACM Author Profile Page?
A. The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM Digital Library. The Author Profile Page supplies a quick snapshot of an author's contribution to the field and some rudimentary measures of influence upon it. Over time, the contents of the Author Profile page may expand at the direction of the community. Please visit the ACM Author Profile documentation page for more background information on these pages.

Q. How do I find my Author Profile page and take ownership?
A. You will need to take the following steps:
- Create a free ACM Web Account
- Sign-In to the ACM Digital Library
- Find your Author Profile Page by searching the ACM Digital Library for your name
- Find the result you authored (where your author name is a clickable link)
- Click on your name to go to the Author Profile Page
- Click the "Add Personal Information" link on the Author Profile Page
- Wait for ACM review and approval; generally less than 24 hours

Q. Why does my photo not appear?
A. Make sure that the image you submit is in .jpg or .gif format and that the file name does not contain special characters

Q. What if I cannot find the Add Personal Information function on my author page?
A. The ACM account linked to your profile page is different than the one you are logged into. Please logout and login to the account associated with your Author Profile Page.

Q. What happens if an author changes the location of his bibliography or moves to a new institution?
A. Should authors change institutions or sites, they can utilize ACM Author-Izer to disable old links and re-authorize new links for free downloads from a new location.

Q. What happens if an author provides a URL that redirects to the author’s personal bibliography page?
A. The service will not provide a free download from the ACM Digital Library. Instead the person who uses that link will simply go to the Citation Page for that article in the ACM Digital Library where the article may be accessed under the usual subscription rules.

However, if the author provides the target page URL, any link that redirects to that target page will enable a free download from the Service.

Q. What happens if the author’s bibliography lives on a page with several aliases?
A. Only one alias will work, whichever one is registered as the page containing the author’s bibliography. ACM has no technical solution to this problem at this time.

Q. Why should authors use ACM Author-Izer?
A. ACM Author-Izer lets visitors to authors’ personal home pages download articles for no charge from the ACM Digital Library. It allows authors to dynamically display real-time download and citation statistics for each “authorized” article on their personal site.

Q. Does ACM Author-Izer provide benefits for authors?
A. Downloads of definitive articles via Author-Izer links on the authors’ personal web page are captured in official ACM statistics to more accurately reflect usage and impact measurements.

Authors who do not use ACM Author-Izer links will not have downloads from their local, personal bibliographies counted. They do, however, retain the existing right to post author-prepared preprint versions on their home pages or institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library.

Q. How does ACM Author-Izer benefit the computing community?
A. ACM Author-Izer expands the visibility and dissemination of the definitive version of ACM articles. It is based on ACM’s strong belief that the computing community should have the widest possible access to the definitive versions of scholarly literature. By linking authors’ personal bibliography with the ACM Digital Library, user confusion over article versioning should be reduced over time.

In making ACM Author-Izer a free service to both authors and visitors to their websites, ACM is emphasizing its continuing commitment to the interests of its authors and to the computing community in ways that are consistent with its existing subscription-based access model.

Q. How do authors access ACM Author-Izer?
A. See How to Use

Q. Why can’t I find my most recent publication in my ACM Author Profile Page?
A. There is a time delay between publication and the process which associates that publication with an Author Profile Page. Right now, that process usually takes 4-8 weeks.

Q. How does ACM Author-Izer expand ACM’s “Green Path” Access Policies?
A. ACM Author-Izer extends the rights and permissions that authors retain even after copyright transfer to ACM, which has been among the “greenest” publishers. ACM enables its author community to retain a wide range of rights related to copyright and reuse of materials. They include:
- Posting rights that ensure free access to their work outside the ACM Digital Library and print publications
- Rights to reuse any portion of their work in new works that they may create
- Copyright to artistic images in ACM’s graphics-oriented publications that authors may want to exploit in commercial contexts
- All patent rights, which remain with the original owner

Wenhui Li

Wenhui Li

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Results

Adaptive Graph Spatial-Temporal Attention Networks for long lead ENSO prediction

Cross-Modal Contrastive Learning with a Style-Mixed Bridge for Single Image 3D Shape Retrieval

Multi-fineness Boundaries and the Shifted Ensemble-aware Encoding for Point Cloud Semantic Segmentation

Dynamic graphs attention for ocean variable forecasting

Dual-Stage Uncertainty Modeling for Unsupervised Cross-Domain 3D Model Retrieval

Progressive Fourier Adversarial Domain Adaptation for Object Classification and Retrieval

Commonsense-Guided Semantic and Relational Consistencies for Image-Text Retrieval

Structured serialization semantic transfer network for unsupervised cross-domain recognition and retrieval

Adaptive semantic transfer network for unsupervised 2D image-based 3D model retrieval

Focus on Hard Samples: Hierarchical Unbiased Constraints for Cross-Domain 3D Model Retrieval

External Knowledge Dynamic Modeling for Image-text Retrieval

Progressive Positive Association Framework for Image and Text Retrieval

Towards Deconfounded Image-Text Matching with Causal Inference

Dual-Path Rare Content Enhancement Network for Image and Text Matching

Instance-prototype similarity consistency for unsupervised 2D image-based 3D model retrieval

Multi-loop graph convolutional network for multimodal conversational emotion recognition

Rare-aware attention network for image–text matching

Prototype-based semantic consistency learning for unsupervised 2D image-based 3D shape retrieval

Semantic Completion and Filtration for Image–Text Retrieval

Cross-Domain Image-Object Retrieval Based on Weighted Optimal Transport