Shangfei Wang

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Shangfei Wang (101)
Qiang Ji (37)
Zhen Gao (9)
Zhilei Liu (8)
Can Wang (7)
Guozhu Peng (7)
Menghua He (7)
Shan He (7)
Bowen Pan (6)
Chongliang Wu (6)
Shi Yin (6)
Xiaoping Chen (6)
Yachen Zhu (6)
Peijia Shen (5)
Shan Wu (5)
Yanan Chang (5)
Bin Xia (4)
Guobing Wu (4)
Tanfang Chen (4)
Yanpeng Lv (4)

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Journal/Magazine Names

IEEE Transactions on Affective Computing (11)
Multimedia Tools and Applications (5)
Pattern Recognition (5)
IEEE Transactions on Multimedia (4)
ACM Transactions on Multimedia Computing, Communications, and Applications (2)
Frontiers of Computer Science: Selected Publications from Chinese Universities (2)
Computer Vision and Image Understanding (1)
IEEE Transactions on Autonomous Mental Development (1)
IEEE Transactions on Image Processing (1)
IEEE Transactions on Pattern Analysis and Machine Intelligence (1)
Machine Vision and Applications (1)

Proceedings/Book Names

MM '20: Proceedings of the 28th ACM International Conference on Multimedia (5)
ACII '13: Proceedings of the 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction (4)
ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval (4)
MM '23: Proceedings of the 31st ACM International Conference on Multimedia (4)
ICPR '14: Proceedings of the 2014 22nd International Conference on Pattern Recognition (3)
MM '22: Proceedings of the 30th ACM International Conference on Multimedia (3)
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2)
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 (2)
ACII'05: Proceedings of the First international conference on Affective Computing and Intelligent Interaction (2)
CSSE '08: Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 01 (2)
MM '17: Proceedings of the 25th ACM international conference on Multimedia (2)
MM '18: Proceedings of the 26th ACM international conference on Multimedia (2)
MM '19: Proceedings of the 27th ACM International Conference on Multimedia (2)
AFFINE '09: Proceedings of the International Workshop on Affective-Aware Virtual Agents and Social Robots (1)
Computer Vision – ACCV 2020 (1)
Computer Vision – ACCV 2022 (1)
Computer Vision – ACCV 2024 (1)
GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation (1)
ICIMCS '11: Proceedings of the Third International Conference on Internet Multimedia Computing and Service (1)
ICMR '16: Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval (1)

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Proceedings (67)
Other periodicals (32)
Journals (2)

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Research Article (56)
Short Paper (5)
Poster (1)

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Association for Computing Machinery (31)
IEEE Computer Society (15)
Springer-Verlag (15)
IEEE Press (12)
IEEE Computer Society Press (11)
Elsevier Science Inc. (6)
AAAI Press (5)
Kluwer Academic Publishers (5)
International Foundation for Autonomous Agents and Multiagent Systems (1)

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Article
Progressive Target Refinement by Self-distillation for Human Pose Estimation
- Jingtian Li
  https://ror.org/04c4dkn09University of Science and Technology of China, Hefei, China
  ,
- Lin Fang
  https://ror.org/04c4dkn09University of Science and Technology of China, Hefei, China
  ,
- Yi Wu
  https://ror.org/04c4dkn09University of Science and Technology of China, Hefei, China
  ,
- Shangfei Wang
  https://ror.org/04c4dkn09University of Science and Technology of China, Hefei, China
Computer Vision – ACCV 2024•December 2024, pp 91-103• https://doi.org/10.1007/978-981-96-0966-6_6
Abstract
The handcrafted heatmap target can be improved and one way is knowledge distillation, which takes the predicted heatmaps from another model as auxiliary supervision. However, previous pose distillation methods are training inefficient, requiring ...
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Temporal Enhancement for Video Affective Content Analysis
- Xin Li
  University of Science and Technology of China, Hefei, Anhui, China
  ,
- Shangfei Wang
  University of Science and Technology of China, Hefei, Anhui, China
  ,
- Xuandong Huang
  University of Science and Technology of China, Hefei, Anhui, China
MM '24: Proceedings of the 32nd ACM International Conference on Multimedia•October 2024, pp 642-650• https://doi.org/10.1145/3664647.3681631

With the popularity and advancement of the Internet and video-sharing platforms, video affective content analysis has greatly developed. Temporal information is crucial for this task. Nevertheless, existing methods often overlook the fact that there is ...
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Pose-robust personalized facial expression recognition through unsupervised multi-source domain adaptation
- Shangfei Wang
  School of Computer Science and Technology, University of Science and Technology of China, 443 HuangShan Rd, Hefei, 230027, Anhui, China
  ,
- Yanan Chang
  School of Computer Science and Technology, University of Science and Technology of China, 443 HuangShan Rd, Hefei, 230027, Anhui, China
  ,
- Qiong Li
  School of Computer Science and Technology, University of Science and Technology of China, 443 HuangShan Rd, Hefei, 230027, Anhui, China
  ,
- Can Wang
  School of Computer Science and Technology, University of Science and Technology of China, 443 HuangShan Rd, Hefei, 230027, Anhui, China
  ,
- Guoming Li
  AI Lab, China Merchants Bank, 27th floor, building D3, Kexing Science Park, Shenzhen, 518000, Guangdong, China
  ,
- Meng Mao
  AI Lab, China Merchants Bank, 27th floor, building D3, Kexing Science Park, Shenzhen, 518000, Guangdong, China
Pattern Recognition, Volume 150, Issue C•Jun 2024 • https://doi.org/10.1016/j.patcog.2024.110311
Abstract
Pose-robust personalized facial expression recognition is rather challenging, as facial expressions are subject-related and pose-dependent. Multi-source domain adaptation tries to leverage knowledge from multiple source domains to boost the ...
Highlights

An unsupervised multi-source domain adaptation method is proposed for FER.
Adversarial learning is used to learn a source FER encoder avoiding pose variations.
Adversarial domain adaptation is used to train a personalized model for ...
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A Multi-Stage Visual Perception Approach for Image Emotion Analysis
- Jicai Pan
  Key Lab of Computing and Communication Software of Anhui Province, School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China
  ,
- Jinqiao Lu
  Key Lab of Computing and Communication Software of Anhui Province, School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China
  ,
- Shangfei Wang
  Key Lab of Computing and Communication Software of Anhui Province, School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China
IEEE Transactions on Affective Computing, Volume 15, Issue 3•July-Sept. 2024, pp 1786-1799 • https://doi.org/10.1109/TAFFC.2024.3372090
Most current methods for image emotion analysis suffer from the affective gap, in which features directly extracted from images are supervised by a single emotional label, which may not align with users’ perceived emotions. To effectively address ...
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UniFaRN: Unified Transformer for Facial Reaction Generation
- Cong Liang
  USTC, Hefei, China
  ,
- Jiahe Wang
  USTC, Hefei, China
  ,
- Haofan Zhang
  USTC, Hefei, China
  ,
- Bing Tang
  USTC, Hefei, China
  ,
- Junshan Huang
  USTC, Hefei, China
  ,
- Shangfei Wang
  USTC, Hefei, China
  ,
- Xiaoping Chen
  USTC, Hefei, China
MM '23: Proceedings of the 31st ACM International Conference on Multimedia•October 2023, pp 9506-9510• https://doi.org/10.1145/3581783.3612854

We propose the Unified Transformer for Facial Reaction GeneratioN (UniFaRN) framework for facial reaction prediction in dyadic interactions. Given the video and audio of one side, the task is to generate facial reactions of the other side. The challenge ...
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MEDIC: A Multimodal Empathy Dataset in Counseling
- Zhouan Zhu
  University of Science and Technology of China, Hefei, China
  ,
- Chenguang Li
  University of Science and Technology of China, Hefei, China
  ,
- Jicai Pan
  University of Science and Technology of China, Hefei, China
  ,
- Xin Li
  University of Science and Technology of China, Hefei, China
  ,
- Yufei Xiao
  University of Science and Technology of China, Hefei, China
  ,
- Yanan Chang
  University of Science and Technology of China, Hefei, China
  ,
- Feiyi Zheng
  University of Science and Technology of China, Hefei, China
  ,
- Shangfei Wang
  University of Science and Technology of China, Hefei, China
MM '23: Proceedings of the 31st ACM International Conference on Multimedia•October 2023, pp 6054-6062• https://doi.org/10.1145/3581783.3612346

Although empathic interaction between counselor and client is fundamental to success in the psychotherapeutic process, there are currently few datasets to aid a computational approach to empathy understanding. In this paper, we construct a multimodal ...
3Citation
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Patch-Aware Representation Learning for Facial Expression Recognition
- Yi Wu
  University of Science and Technology of China, Hefei, China
  ,
- Shangfei Wang
  University of Science and Technology of China, Hefei, China
  ,
- Yanan Chang
  University of Science and Technology of China, Hefei, China
MM '23: Proceedings of the 31st ACM International Conference on Multimedia•October 2023, pp 6143-6151• https://doi.org/10.1145/3581783.3612342

Existing methods for facial expression recognition (FER) lack the utilization of prior facial knowledge, primarily focusing on expression-related regions while disregarding explicitly processing expression-independent information. This paper proposes a ...
3Citation
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Progressive Visual Content Understanding Network for Image Emotion Classification
- Jicai Pan
  University of Science and Technology of China, Hefei, China
  ,
- Shangfei Wang
  University of Science and Technology of China & Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
MM '23: Proceedings of the 31st ACM International Conference on Multimedia•October 2023, pp 6034-6044• https://doi.org/10.1145/3581783.3612186

Most existing methods for image emotion classification extract features directly from images supervised by a single emotional label. However, this approach has a limitation known as the affective gap which restricts the capability of these features as ...
2Citation
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Pose-Aware Facial Expression Recognition Assisted by Expression Descriptions
- Shangfei Wang
  Key Lab of Computing and Communication Software of Anhui Provice, School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China
  ,
- Yi Wu
  Key Lab of Computing and Communication Software of Anhui Provice, School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China
  ,
- Yanan Chang
  Key Lab of Computing and Communication Software of Anhui Provice, School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China
  ,
- Guoming Li
  AI Lab, China Merchants Bank, Shenzhen, Guangdong, China
  ,
- Meng Mao
  AI Lab, China Merchants Bank, Shenzhen, Guangdong, China
IEEE Transactions on Affective Computing, Volume 15, Issue 1•Jan.-March 2024, pp 241-253 • https://doi.org/10.1109/TAFFC.2023.3267774
Although expression descriptions provide additional information about facial behaviors despite of different poses, and pose features are beneficial to adapt to pose variety, neither has been fully leveraged in facial expression recognition. This paper ...
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Dual Learning for Joint Facial Landmark Detection and Action Unit Recognition
- Shangfei Wang
  Key Lab of Computing and Communication Software of Anhui Province, School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China
  ,
- Yanan Chang
  Key Lab of Computing and Communication Software of Anhui Province, School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China
  ,
- Can Wang
  Key Lab of Computing and Communication Software of Anhui Province, School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China
IEEE Transactions on Affective Computing, Volume 14, Issue 2•April-June 2023, pp 1404-1416 • https://doi.org/10.1109/TAFFC.2021.3114158
Facial landmark detection and action unit (AU) recognition are two essential tasks in facial analysis. Previous works rarely consider the relationship between these complementary tasks. In this article, we introduce a novel multi-task dual learning ...
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Occluded Facial Expression Recognition Using Self-supervised Learning
- Jiahe Wang
  Key Lab of Computing and Communication Software of Anhui Province, University of Science and Technology of China, Hefei, China
  ,
- Heyan Ding
  Key Lab of Computing and Communication Software of Anhui Province, University of Science and Technology of China, Hefei, China
  ,
- Shangfei Wang
  Key Lab of Computing and Communication Software of Anhui Province, University of Science and Technology of China, Hefei, China
  Anhui Robot Technology Standard Innovation Base, University of Science and Technology of China, Hefei, China
Computer Vision – ACCV 2022•December 2022, pp 121-136• https://doi.org/10.1007/978-3-031-26316-3_8
Abstract
Recent studies on occluded facial expression recognition typically required fully expression-annotated facial images for training. However, it is time consuming and expensive to collect a large number of facial images with various occlusions and ...
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Low-Resolution Face Recognition Enhanced by High-Resolution Facial Images
- Haihan Wang
  University of Science and Technology of China,Key Lab of Computing and Communication Software of Anhui Province
  ,
- Shangfei Wang
  University of Science and Technology of China,Key Lab of Computing and Communication Software of Anhui Province
2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)•January 2023, pp 1-8• https://doi.org/10.1109/FG57933.2023.10042552
Despite recent advances in high-resolution (HR) face recognition, recognizing identities from low-resolution (LR) facial images remains challenging due to the absence of facial shape and detail. Current research focuses solely on reducing the distribution ...
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Emotional Attention Detection and Correlation Exploration for Image Emotion Distribution Learning
- Zhiwei Xu
  Key Lab of Computing and Communication Software of Anhui Province, Department of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China
  ,
- Shangfei Wang
  Key Lab of Computing and Communication Software of Anhui Province, Department of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China
IEEE Transactions on Affective Computing, Volume 14, Issue 1•Jan.-March 2023, pp 357-369 • https://doi.org/10.1109/TAFFC.2021.3071131
Current works on image emotion distribution learning typically extract visual representations from the holistic image or explore emotion-related regions in the image from a global-wise perspective. However, different regions of an image contribute ...
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Two-Stage Multi-Scale Resolution-Adaptive Network for Low-Resolution Face Recognition
- Haihan Wang
  University of Science and Technology of China, Hefei, China
  ,
- Shangfei Wang
  University of Science and Technology of China, Hefei, China
  ,
- Lin Fang
  University of Science and Technology of China, Hefei, China
MM '22: Proceedings of the 30th ACM International Conference on Multimedia•October 2022, pp 4053-4062• https://doi.org/10.1145/3503161.3548196

Low-resolution face recognition is challenging due to uncertain input resolutions and the lack of distinguishing details in low-resolution (LR) facial images. Resolution-invariant representations must be learned for optimal performance. Existing methods ...
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Knowledge Guided Representation Disentanglement for Face Recognition from Low Illumination Images
- Xiangyu Miao
  University of Science and Technology of China, Hefei, China
  ,
- Shangfei Wang
  University of Science and Technology of China, Hefei, China
MM '22: Proceedings of the 30th ACM International Conference on Multimedia•October 2022, pp 6655-6663• https://doi.org/10.1145/3503161.3548174

Low illumination face recognition is challenging as details are lacking due to lighting conditions. Retinex theory points out that images can be divided into reflectance with color constancy and ambient illumination. Inspired by this, we propose a ...
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Representation Learning through Multimodal Attention and Time-Sync Comments for Affective Video Content Analysis
- Jicai Pan
  University of Science and Technology of China, Hefei, China
  ,
- Shangfei Wang
  University of Science and Technology of China, Hefei, China
  ,
- Lin Fang
  University of Science and Technology of China, Hefei, China
MM '22: Proceedings of the 30th ACM International Conference on Multimedia•October 2022, pp 42-50• https://doi.org/10.1145/3503161.3548018

Although temporal patterns inherent in visual and audio signals are crucial for affective video content analysis, they have not been thoroughly explored yet. In this paper, we propose a novel Temporal-Aware Multimodal (TAM) method to fully capture the ...
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Pose-Invariant Facial Expression Recognition
- Guang Liang
  University of Science and Technology of China
  ,
- Shangfei Wang
  University of Science and Technology of China
  ,
- Can Wang
  University of Science and Technology of China
2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)•December 2021, pp 01-08• https://doi.org/10.1109/FG52635.2021.9666974
Pose-invariant facial expression recognition is quite challenging due to variations in facial appearance and self-occlusion caused by head rotations. In this paper, we propose an adversarial multi-view subspace learning method for pose-robust facial ...
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Deep Facial Action Unit Recognition and Intensity Estimation from Partially Labelled Data
- Shangfei Wang
  Key Lab of Computing and Communication Software of Anhui Province, School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, PR China
  ,
- Bowen Pan
  Key Lab of Computing and Communication Software of Anhui Province, School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, PR China
  ,
- Shan Wu
  Key Lab of Computing and Communication Software of Anhui Province, School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, PR China
  ,
- Qiang Ji
  Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
IEEE Transactions on Affective Computing, Volume 12, Issue 4•Oct.-Dec. 2021, pp 1018-1030 • https://doi.org/10.1109/TAFFC.2019.2914654
Research on facial action unit (AU) analysis typically require facial images that are labelled with those action units. While unlabelled facial images abound, labelling those images with action units or intensity is costly and time-consuming. Our approach ...
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Video Affective Content Analysis by Exploring Domain Knowledge
- Shangfei Wang
  Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China
  ,
- Can Wang
  Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China
  ,
- Tanfang Chen
  Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China
  ,
- Yaxin Wang
  Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China
  ,
- Yangyang Shu
  Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China
  ,
- Qiang Ji
  Department of Electrical, Computer and System Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
IEEE Transactions on Affective Computing, Volume 12, Issue 4•Oct.-Dec. 2021, pp 1002-1017 • https://doi.org/10.1109/TAFFC.2019.2912377
Film grammar is often used to invoke certain emotional experiences from audiences through changing visual, speech, and musical elements of videos. Such film grammar, referred to as domain knowledge, is of great importance for video affective content ...
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Capturing Emotion Distribution for Multimedia Emotion Tagging
- Shangfei Wang
  Department of Computer Science and Technology, Key Lab of Computing and Communication Software of Anhui Province, University of Science and Technology of China, Hefei, Anhui, China
  ,
- Guozhu Peng
  Department of Computer Science and Technology, Key Lab of Computing and Communication Software of Anhui Province, University of Science and Technology of China, Hefei, Anhui, China
  ,
- Zhuangqiang Zheng
  Department of Computer Science and Technology, Key Lab of Computing and Communication Software of Anhui Province, University of Science and Technology of China, Hefei, Anhui, China
  ,
- Zhiwei Xu
  Department of Computer Science and Technology, Key Lab of Computing and Communication Software of Anhui Province, University of Science and Technology of China, Hefei, Anhui, China
IEEE Transactions on Affective Computing, Volume 12, Issue 4•Oct.-Dec. 2021, pp 821-831 • https://doi.org/10.1109/TAFFC.2019.2900240
Multimedia collections usually induce multiple emotions in audiences. The data distribution of multiple emotions can be leveraged to facilitate the learning process of emotion tagging, yet has not been thoroughly explored. To address this, we propose ...
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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