Guiguang Ding
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- IEEE Transactions on Image Processing (8)
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- AAAI'19/IAAI'19/EAAI'19: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence (5)
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- AAAI'16: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (2)
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- CVPR '14: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (2)
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- IIH-MSP '08: Proceedings of the 2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing (2)
- IJCAI'18: Proceedings of the 27th International Joint Conference on Artificial Intelligence (2)
- IJCAI'18: Proceedings of the 27th International Joint Conference on Artificial Intelligence (2)
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- research-articlePublished By ACMPublished By ACM
Multi-Label Learning with Block Diagonal Labels
- Leqi Shen
School of Software, BNRist, Tsinghua University, Beijing, China
, - Sicheng Zhao
BNRist, Tsinghua University, Beijing, China
, - Yifeng Zhang
jd.com, Beijing, China
, - Hui Chen
BNRist, Tsinghua University, Beijing, China
, - Jundong Zhou
School of Software, BNRist, Tsinghua University, Beijing, China
, - Pengzhang Liu
jd.com, Beijing, China
, - Yongjun Bao
jd.com, Beijing, China
, - Guiguang Ding
School of Software, BNRist, Tsinghua University, Beijing, China
MM '24: Proceedings of the 32nd ACM International Conference on Multimedia•October 2024, pp 4832-4840• https://doi.org/10.1145/3664647.3680793Collecting large-scale multi-label data with full labels is difficult for real-world scenarios. Many existing studies have tried to address the issue of missing labels caused by annotation but ignored the difficulties encountered during the annotation ...
- 0Citation
- 42
- Downloads
MetricsTotal Citations0Total Downloads42Last 12 Months42Last 6 weeks17
- Leqi Shen
- Article
PYRA: Parallel Yielding Re-activation for Training-Inference Efficient Task Adaptation
- Yizhe Xiong
https://ror.org/03cve4549School of Software, Tsinghua University, Beijing, China
https://ror.org/03cve4549BNRist, Tsinghua University, Beijing, China
, - Hui Chen
https://ror.org/03cve4549BNRist, Tsinghua University, Beijing, China
, - Tianxiang Hao
https://ror.org/03cve4549School of Software, Tsinghua University, Beijing, China
https://ror.org/03cve4549BNRist, Tsinghua University, Beijing, China
, - Zijia Lin
https://ror.org/03cve4549School of Software, Tsinghua University, Beijing, China
, - Jungong Han
https://ror.org/03cve4549BNRist, Tsinghua University, Beijing, China
https://ror.org/03cve4549Department of Automation, Tsinghua University, Beijing, China
, - Yuesong Zhang
JD.com, Beijing, China
, - Guoxin Wang
JD.com, Beijing, China
, - Yongjun Bao
JD.com, Beijing, China
, - Guiguang Ding
https://ror.org/03cve4549School of Software, Tsinghua University, Beijing, China
https://ror.org/03cve4549BNRist, Tsinghua University, Beijing, China
Computer Vision – ECCV 2024•September 2024, pp 455-473• https://doi.org/10.1007/978-3-031-72673-6_25AbstractRecently, the scale of transformers has grown rapidly, which introduces considerable challenges in terms of training overhead and inference efficiency in the scope of task adaptation. Existing works, namely Parameter-Efficient Fine-Tuning (PEFT) ...
- 0Citation
MetricsTotal Citations0
- Yizhe Xiong
- Article
Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual Persistence
- Mengyao Lyu
https://ror.org/03cve4549Tsinghua University, Beijing, China
BNRist, Beijing, China
, - Tianxiang Hao
https://ror.org/03cve4549Tsinghua University, Beijing, China
BNRist, Beijing, China
, - Xinhao Xu
https://ror.org/03cve4549Tsinghua University, Beijing, China
BNRist, Beijing, China
, - Hui Chen
https://ror.org/03cve4549Tsinghua University, Beijing, China
BNRist, Beijing, China
, - Zijia Lin
https://ror.org/03cve4549Tsinghua University, Beijing, China
, - Jungong Han
https://ror.org/03cve4549Tsinghua University, Beijing, China
BNRist, Beijing, China
, - Guiguang Ding
https://ror.org/03cve4549Tsinghua University, Beijing, China
BNRist, Beijing, China
Computer Vision – ECCV 2024•September 2024, pp 228-246• https://doi.org/10.1007/978-3-031-73232-4_13AbstractDomain Adaptation (DA) facilitates knowledge transfer from a source domain to a related target domain. This paper investigates a practical DA paradigm, namely Source data-Free Active Domain Adaptation (SFADA), where source data becomes ...
- 0Citation
MetricsTotal Citations0
- Mengyao Lyu
- posterOpen AccessPublished By ACMPublished By ACM
Open-world Domain Adaptation and Generalization
- Sicheng Zhao
BNRist, Tsinghua University, China
, - Jianhua Tao
BNRist, Department of Automation, Tsinghua University, China
, - Guiguang Ding
BNRist, School of Software, Tsinghua University, China
ACM-TURC '24: Proceedings of the ACM Turing Award Celebration Conference - China 2024•July 2024, pp 201-202• https://doi.org/10.1145/3674399.3674462Deep learning has achieved unprecedented success in various artificial intelligence areas and tasks. One precondition is that large-scale labeled training data is provided to train a neural network. Although recent self-supervised pre-training can ...
- 0Citation
- 222
- Downloads
MetricsTotal Citations0Total Downloads222Last 12 Months222Last 6 weeks46
- Sicheng Zhao
- research-article
Confidence-Guided Centroids for Unsupervised Person Re-Identification
- Yunqi Miao
Warwick Manufacturing Group (WMG), The University of Warwick, Coventry, U.K.
, - Jiankang Deng
Department of Computing, Imperial College London, London, U.K.
, - Guiguang Ding
School of Software, Tsinghua University, Beijing, China
, - Jungong Han
Department of Computer Science, The University of Sheffield, Sheffield, U.K.
IEEE Transactions on Information Forensics and Security, Volume 19•2024, pp 6471-6483 • https://doi.org/10.1109/TIFS.2024.3414310Unsupervised person re-identification (ReID) aims to train a feature extractor for identity retrieval without exploiting identity labels. Due to the no-reference trust in imperfect clustering results, the learning is inevitably misled by unreliable pseudo ...
- 0Citation
MetricsTotal Citations0
- Yunqi Miao
- extended-abstract
JDRec: Practical Actor-Critic Framework for Online Combinatorial Recommender System
- Xin Zhao
Tsinghua University, Beijing, China
, - Jiaxin Li
Tsinghua University, Beijing, China
, - Zhiwei Fang
JD.com, Beijing, China
, - Yuchen Guo
Tsinghua University, Beijing, China
, - Jinyuan Zhao
JD.com, Beijing, China
, - Jie He
JD.com, Beijing, China
, - Wenlong Chen
JD.com, Beijing, China
, - Changping Peng
JD.com, Beijing, China
, - Guiguang Ding
Tsinghua University, Beijing, China
AAMAS '24: Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems•May 2024, pp 2612-2614In the realm of online recommendation systems, the Combinatorial Recommender (CR) system stands out for its unique approach. It presents users with a list of items on a result page, where user behavior is simultaneously influenced by contextual ...
- 0Citation
- 14
- Downloads
MetricsTotal Citations0Total Downloads14Last 12 Months14Last 6 weeks2
- Xin Zhao
- research-article
GPro3D: Deriving 3D BBox from ground plane in monocular 3D object detection
- Fan Yang
School of Software, Tsinghua University, Beijing, China
BNRist, Tsinghua University, Beijing, China
, - Xinhao Xu
School of Software, Tsinghua University, Beijing, China
BNRist, Tsinghua University, Beijing, China
, - Hui Chen
BNRist, Tsinghua University, Beijing, China
, - Yuchen Guo
BNRist, Tsinghua University, Beijing, China
, - Yuwei He
School of Software, Tsinghua University, Beijing, China
BNRist, Tsinghua University, Beijing, China
, - Kai Ni
HoloMatic Technology, Beijing, China
, - Guiguang Ding
School of Software, Tsinghua University, Beijing, China
BNRist, Tsinghua University, Beijing, China
AbstractConsidering the inherent ill-posed nature, monocular 3D object detection (M3OD) is extremely challenging. The ground plane prior is a highly informative geometry clue in M3OD. However, it has been neglected by most mainstream methods. This paper ...
- 0Citation
MetricsTotal Citations0
- Fan Yang
- research-articleOpen AccessPublished By ACMPublished By ACM
Hierarchical Prompt Learning Using CLIP for Multi-label Classification with Single Positive Labels
- Ao Wang
Tsinghua University, Beijing, China
, - Hui Chen
Tsinghua University, Beijing, China
, - Zijia Lin
Tsinghua University, Beijing, China
, - Zixuan Ding
Xidian University & Zhuoxi Institute of Brain and Intelligence, Beijing & Hangzhou, China
, - Pengzhang Liu
jd.com, Beijing, China
, - Yongjun Bao
jd.com, Beijing, China
, - Weipeng Yan
jd.com, Beijing, China
, - Guiguang Ding
Tsinghua University, Beijing, China
MM '23: Proceedings of the 31st ACM International Conference on Multimedia•October 2023, pp 5594-5604• https://doi.org/10.1145/3581783.3611988Collecting full annotations to construct multi-label datasets is difficult and labor-consuming. As an effective solution to relieve the annotation burden, single positive multi-label learning (SPML) draws increasing attention from both academia and ...
- 4Citation
- 1,408
- Downloads
MetricsTotal Citations4Total Downloads1,408Last 12 Months1,137Last 6 weeks98
- Ao Wang
- research-article
Margin-aware rectified augmentation for long-tailed recognition
- Liuyu Xiang
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
, - Jungong Han
Department of Computer Science, Aberystwyth University, UK
, - Guiguang Ding
Beijing National Research Center for Information Science and Technology (BNRist), School of Software, Tsinghua University, Beijing 100084, China
Highlights- Long-tailed distribution leads to decision boundary bias in deep neural networks.
AbstractThe long-tailed data distribution is prevalent in real world and it poses great challenge on deep neural network training. In this paper, we propose Margin-aware Rectified Augmentation (MRA) to tackle this problem. Specifically, the ...
- 1Citation
MetricsTotal Citations1
- Liuyu Xiang
- unknown
Long-tailed visual recognition with deep models: A methodological survey and evaluation
- Yu Fu
Aberystwyth University, Computer Science Department, Aberystwyth SY23 3DB, Ceredigion, UK
, - Liuyu Xiang
Tsinghua University, School of Software, Beijing, China
, - Yumna Zahid
Aberystwyth University, Computer Science Department, Aberystwyth SY23 3DB, Ceredigion, UK
, - Guiguang Ding
Tsinghua University, School of Software, Beijing, China
, - Tao Mei
JD, AI Research, Beijing, China
, - Qiang Shen
Aberystwyth University, Computer Science Department, Aberystwyth SY23 3DB, Ceredigion, UK
, - Jungong Han
Aberystwyth University, Computer Science Department, Aberystwyth SY23 3DB, Ceredigion, UK
Neurocomputing, Volume 509, Issue C•Oct 2022, pp 290-309 • https://doi.org/10.1016/j.neucom.2022.08.031AbstractIn the real world, large-scale datasets for visual recognition typically exhibit a long-tailed distribution, where only a few classes contain adequate samples but the others have (much) fewer samples. With the advancement of data-...
- 1Citation
MetricsTotal Citations1
- Yu Fu
- research-articleOpen AccessPublished By ACMPublished By ACM
TAGPerson: A Target-Aware Generation Pipeline for Person Re-identification
- Kai Chen
Tsinghua University, Beijing, China
, - Weihua Chen
Alibaba Group, Beijing, China
, - Tao He
Tsinghua University, Beijing, China
, - Rong Du
Alibaba Group, Shanghai, China
, - Fan Wang
Alibaba Group, Sunnyvale, CA, USA
, - Xiuyu Sun
Alibaba Group, Beijing, China
, - Yuchen Guo
Beijing National Research Center for Information Science and Technology (BNRist), Beijing, China
, - Guiguang Ding
Tsinghua University, Beijing, China
MM '22: Proceedings of the 30th ACM International Conference on Multimedia•October 2022, pp 560-571• https://doi.org/10.1145/3503161.3548013Nowadays, real data in person re-identification (ReID) task is facing privacy issues, e.g., the banned dataset DukeMTMC-ReID. Thus it becomes much harder to collect real data for ReID task. Meanwhile, the labor cost of labeling ReID data is still very ...
- 4Citation
- 498
- Downloads
MetricsTotal Citations4Total Downloads498Last 12 Months116Last 6 weeks16- 1
Supplementary MaterialMM22-fp1120.mp4
- Kai Chen
- research-article
Affective Image Content Analysis: Two Decades Review and New Perspectives
- Sicheng Zhao
BNRist, Tsinghua University, Beijing, China
, - Xingxu Yao
College of Computer Science, Nankai University, Tianjin, China
, - Jufeng Yang
College of Computer Science, Nankai University, Tianjin, China
, - Guoli Jia
College of Computer Science, Nankai University, Tianjin, China
, - Guiguang Ding
BNRist, Tsinghua University, Beijing, China
, - Tat-Seng Chua
School of Computing, National University of Singapore, Singapore, Singapore
, - Björn W. Schuller
Department of Computing, Imperial College London, London, U.K.
, - Kurt Keutzer
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA
IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 44, Issue 10_Part_2•Oct. 2022, pp 6729-6751 • https://doi.org/10.1109/TPAMI.2021.3094362Images can convey rich semantics and induce various emotions in viewers. Recently, with the rapid advancement of emotional intelligence and the explosive growth of visual data, extensive research efforts have been dedicated to affective image content ...
- 24Citation
MetricsTotal Citations24
- Sicheng Zhao
- research-article
Deep learning for video object segmentation: a review
- Mingqi Gao
WMG Data Science, University of Warwick, CV4 7AL, Coventry, UK
Department of Computer Science and Engineering, Southern University of Science and Technology, 518055, Shenzhen, China
, - Feng Zheng
Department of Computer Science and Engineering, Southern University of Science and Technology, 518055, Shenzhen, China
, - James J. Q. Yu
Department of Computer Science and Engineering, Southern University of Science and Technology, 518055, Shenzhen, China
, - Caifeng Shan
College of Electrical Engineering and Automation, Shandong University of Science and Technology, 266590, Qingdao, China
, - Guiguang Ding
School of Software, Tsinghua University, 100084, Beijing, China
, - Jungong Han
WMG Data Science, University of Warwick, CV4 7AL, Coventry, UK
Department of Computer Science, Aberystwyth University, SY23 3DB, Aberystwyth, UK
Artificial Intelligence Review, Volume 56, Issue 1•Jan 2023, pp 457-531 • https://doi.org/10.1007/s10462-022-10176-7AbstractAs one of the fundamental problems in the field of video understanding, video object segmentation aims at segmenting objects of interest throughout the given video sequence. Recently, with the advancements of deep learning techniques, deep neural ...
- 3Citation
MetricsTotal Citations3
- Mingqi Gao
- research-article
Towards real-time object detection in GigaPixel-level video
- Kai Chen
Tsinghua University, Haidian, Beijing 100084, China
, - Zerun Wang
Tsinghua University, Haidian, Beijing 100084, China
, - Xueyang Wang
Tsinghua University, Haidian, Beijing 100084, China
, - Dahan Gong
Tsinghua University, Haidian, Beijing 100084, China
, - Longlong Yu
Tsinghua University, Haidian, Beijing 100084, China
, - Yuchen Guo
Tsinghua University, Haidian, Beijing 100084, China
, - Guiguang Ding
Tsinghua University, Haidian, Beijing 100084, China
Neurocomputing, Volume 477, Issue C•Mar 2022, pp 14-24 • https://doi.org/10.1016/j.neucom.2021.12.049Graphical abstractDisplay Omitted
Highlights- GigaPixel-level images contain rich information for analysis in surveillance situation.
AbstractObject detection aims to locate and recognize objects in images or videos, which contributes to many downstream intelligent applications. Recently, emerging gigapixel videography has attracted considerable attention from computer ...
- 1Citation
MetricsTotal Citations1
- Kai Chen
- research-article
Deep image compression with multi-stage representation
- Zixi Wang
School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, China
, - Guiguang Ding
School of Software, Tsinghua University, Beijing 100084, China
, - Jungong Han
Computer Science Department, Aberystwyth University, Aberystwyth SY23 3FL, UK
, - Fan Li
School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Journal of Visual Communication and Image Representation, Volume 79, Issue C•Aug 2021 • https://doi.org/10.1016/j.jvcir.2021.103226AbstractWhile deep learning-based image compression methods have shown impressive coding performance, most existing methods are still in the mire of two limitations: (1) unpredictable compression efficiency gain when adopting convolutional ...
Highlights- Extracting multi-stage representation of input images improves the compression efficiency.
- 1Citation
MetricsTotal Citations1
- Zixi Wang
- research-article
MADAN: Multi-source Adversarial Domain Aggregation Network for Domain Adaptation
- Sicheng Zhao
BNRist, Tsinghua University, Beijing, China
, - Bo Li
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, USA
, - Pengfei Xu
Didi Chuxing, Beijing, China
, - Xiangyu Yue
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, USA
, - Guiguang Ding
BNRist, Tsinghua University, Beijing, China
, - Kurt Keutzer
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, USA
International Journal of Computer Vision, Volume 129, Issue 8•Aug 2021, pp 2399-2424 • https://doi.org/10.1007/s11263-021-01479-3AbstractDomain adaptation aims to learn a transferable model to bridge the domain shift between one labeled source domain and another sparsely labeled or unlabeled target domain. Since the labeled data may be collected from multiple sources, multi-source ...
- 11Citation
MetricsTotal Citations11
- Sicheng Zhao
- Article
Increasing Oversampling Diversity for Long-Tailed Visual Recognition
- Liuyu Xiang
School of Software, Tsinghua University, Beijing, China
Beijing National Research Center for Information Science and Technology (BNRist), Beijing, China
, - Guiguang Ding
School of Software, Tsinghua University, Beijing, China
Beijing National Research Center for Information Science and Technology (BNRist), Beijing, China
, - Jungong Han
Computer Science Department, Aberystwyth University, SY23 3FL, Aberystwyth, UK
AbstractThe long-tailed data distribution in real-world greatly increases the difficulty of training deep neural networks. Oversampling minority classes is one of the commonly used techniques to tackle this problem. In this paper, we first analyze that ...
- 0Citation
MetricsTotal Citations0
- Liuyu Xiang
- research-article
Dynamic Selective Network for RGB-D Salient Object Detection
- Hongfa Wen
School of Automation, Hangzhou Dianzi University, Hangzhou, China
, - Chenggang Yan
School of Automation, Hangzhou Dianzi University, Hangzhou, China
, - Xiaofei Zhou
School of Automation, Hangzhou Dianzi University, Hangzhou, China
, - Runmin Cong
Institute of Information Science, Beijing Jiaotong University, Beijing, China
, - Yaoqi Sun
School of Automation, Hangzhou Dianzi University, Hangzhou, China
, - Bolun Zheng
School of Automation, Hangzhou Dianzi University, Hangzhou, China
, - Jiyong Zhang
School of Automation, Hangzhou Dianzi University, Hangzhou, China
, - Yongjun Bao
Business Growth BU, JD.com, Beijing, China
, - Guiguang Ding
School of Software, Tsinghua University, Beijing, China
IEEE Transactions on Image Processing, Volume 30•2021, pp 9179-9192 • https://doi.org/10.1109/TIP.2021.3123548RGB-D saliency detection is receiving more and more attention in recent years. There are many efforts have been devoted to this area, where most of them try to integrate the multi-modal information, <italic>i.e.</italic> RGB images and depth maps, via ...
- 20Citation
MetricsTotal Citations20
- Hongfa Wen
- research-article
Where to Prune: Using LSTM to Guide Data-Dependent Soft Pruning
- Guiguang Ding
School of Software, Tsinghua University, Beijing, China
, - Shuo Zhang
School of Computing and Communications, Lancaster University, Lancaster, U.K.
, - Zizhou Jia
School of Software, Tsinghua University, Beijing, China
, - Jing Zhong
School of Software, Tsinghua University, Beijing, China
, - Jungong Han
Department of Computer Science, Aberystwyth University, Aberystwyth, U.K.
IEEE Transactions on Image Processing, Volume 30•2021, pp 293-304 • https://doi.org/10.1109/TIP.2020.3035028While convolutional neural network (CNN) has achieved overwhelming success in various vision tasks, its heavy computational cost and storage overhead limit the practical use on mobile or embedded devices. Recently, compressing CNN models has attracted ...
- 12Citation
MetricsTotal Citations12
- Guiguang Ding
- research-article
Discrete Probability Distribution Prediction of Image Emotions with Shared Sparse Learning
- Sicheng Zhao
School of Software, Tsinghua University, Beijing, China
, - Guiguang Ding
School of Software, Tsinghua University, Beijing, China
, - Yue Gao
School of Software, Tsinghua University, Beijing, China
, - Xin Zhao
School of Software, Tsinghua University, Beijing, China
, - Youbao Tang
National Institutes of Health, Bethesda, MD
, - Jungong Han
School of Computing & Communications, Lancaster University, Lancaster, United Kingdom
, - Hongxun Yao
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
, - Qingming Huang
School of Computer and Control Engineering, University of Chinese Academy of Sciences, Huairou, China
IEEE Transactions on Affective Computing, Volume 11, Issue 4•Oct.-Dec. 2020, pp 574-587 • https://doi.org/10.1109/TAFFC.2018.2818685Computationally modelling the affective content of images has been extensively studied recently because of its wide applications in entertainment, advertisement, and education. Significant progress has been made on designing discriminative features to ...
- 12Citation
MetricsTotal Citations12
- Sicheng Zhao
Author Profile Pages
- 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.
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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. 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