Ping Li

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Ping Li (40)
Jiajun Bu (8)
Xianghua Xu (8)
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Hong Li (3)
Li Yuan (3)
Min Wu (3)
Zhanying He (3)
Bin Xu (2)
Bo Li (2)
Chunyue Song (2)
Guopan Zhao (2)
Jianyuan Ruan (2)
Jiashi Feng (2)
Luming Zhang (2)
Wenjie Zhao (2)
Yu Zhang (2)
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Neurocomputing (5)
Expert Systems with Applications: An International Journal (3)
Information Sciences: an International Journal (3)
Autonomous Robots (2)
Information Processing and Management: an International Journal (2)
Pattern Recognition (2)
Pattern Recognition Letters (2)
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Automatica (Journal of IFAC) (1)
IEEE Transactions on Circuits and Systems for Video Technology (1)
IEEE Transactions on Image Processing (1)
IEEE Transactions on Pattern Analysis and Machine Intelligence (1)
Journal of Visual Communication and Image Representation (1)
Knowledge and Information Systems (1)
Knowledge-Based Systems (1)
Neural Networks (1)
Signal Processing (1)

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ISNN'06: Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I (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 (1)
ACC'09: Proceedings of the 2009 conference on American Control Conference (1)
CCDC'09: Proceedings of the 21st annual international conference on Chinese Control and Decision Conference (1)
CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management (1)
ICIC'05: Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I (1)
ICONIP'06: Proceedings of the 13 international conference on Neural Information Processing - Volume Part I (1)
ICYCS '08: Proceedings of the 2008 The 9th International Conference for Young Computer Scientists (1)
IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society (1)
IJCAI'17: Proceedings of the 26th International Joint Conference on Artificial Intelligence (1)

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research-article
Bridging knowledge distillation gap for few-sample unsupervised semantic segmentation
- Ping Li
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
  State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
  ,
- Junjie Chen
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
  ,
- Chen Tang
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
Information Sciences: an International Journal, Volume 673, Issue C•Jul 2024 • https://doi.org/10.1016/j.ins.2024.120714
Abstract
Due to privacy, security, and costly labeling of images, unsupervised semantic segmentation with very few samples has become a promising direction, but still remains unexplored. This inspires us to introduce the few-sample unsupervised semantic ...
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research-article
Efficient Long-Short Temporal Attention network for unsupervised Video Object Segmentation
- Ping Li
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
  Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China
  ,
- Yu Zhang
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
  ,
- Li Yuan
  School of Electrical and Computer Engineering, Peking University, Beijing, China
  ,
- Huaxin Xiao
  College of Systems Engineering, National University of Defense Technology, Changsha, China
  ,
- Binbin Lin
  School of Software Technology, Zhejiang University, Hangzhou, China
  ,
- Xianghua Xu
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
Pattern Recognition, Volume 146, Issue C•Feb 2024 • https://doi.org/10.1016/j.patcog.2023.110078
Abstract
Unsupervised Video Object Segmentation (VOS) aims at identifying the contours of primary foreground objects in videos without any prior knowledge. However, previous methods do not fully use spatial–temporal context and fail to tackle this ...
Highlights

Efficient Long-Short Temporal Attention network (LSTA) is developed for unsupervised video object segmentation.
The Long Temporal Memory (LTM) module captures the long-term global pixel relations.
The Short Temporal Attention (STA) ...
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research-article
Fully Transformer-Equipped Architecture for end-to-end Referring Video Object Segmentation
- Ping Li
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
  Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China
  ,
- Yu Zhang
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
  ,
- Li Yuan
  School of Electronic and Computer Engineering, Peking University, Beijing, China
  ,
- Xianghua Xu
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
Information Processing and Management: an International Journal, Volume 61, Issue 1•Jan 2024 • https://doi.org/10.1016/j.ipm.2023.103566
Abstract
Referring Video Object Segmentation (RVOS) requires segmenting the object in video referred by a natural language query. Existing methods mainly rely on sophisticated pipelines to tackle such cross-modal task, and do not explicitly model the ...
Highlights

An end-to-end RVOS framework named Fully Transformer-Equipped Architecture (FTEA) is developed completely upon transformers.
The stacked attention module captures the object-level spatial context, and the stacked Feed-Forward Network ...
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research-article
Adversarial Attacks on Video Object Segmentation With Hard Region Discovery
- Ping Li
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
  ,
- Yu Zhang
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
  ,
- Li Yuan
  School of Electronic and Computer Engineering, Peking University, Beijing, China
  ,
- Jian Zhao
  Peng Cheng Laboratory, Shenzhen, China
  ,
- Xianghua Xu
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
  ,
- Xiaoqin Zhang
  College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China
IEEE Transactions on Circuits and Systems for Video Technology, Volume 34, Issue 6•June 2024, pp 5049-5062 • https://doi.org/10.1109/TCSVT.2023.3341170
Video object segmentation has been applied to various computer vision tasks, such as video editing, autonomous driving, and human-robot interaction. However, the methods based on deep neural networks are vulnerable to adversarial examples, which are the ...
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research-article
Truncated attention-aware proposal networks with multi-scale dilation for temporal action detection
- Ping Li
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
  State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
  ,
- Jiachen Cao
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
  ,
- Li Yuan
  School of Electrical and Computer Engineering, Peking University, Beijing, China
  ,
- Qinghao Ye
  University of California, San Diego, USA
  ,
- Xianghua Xu
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
Pattern Recognition, Volume 142, Issue C•Oct 2023 • https://doi.org/10.1016/j.patcog.2023.109684
Highlights

Multi-scale Dilation based Truncated Attention Proposal Network (MD-TAPN) is developed for detecting actions along the temporal dimension in video.
A truncated attention mechanism is designed to encourage positive proposal relations by ...
Abstract
Detecting actions temporally in untrimmed videos is very challenging, and it accomplishes action classification and localization simultaneously. Capturing the relations among action proposals (i.e., candidate video segments) is of vital ...
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research-article
Time–frequency recurrent transformer with diversity constraint for dense video captioning
- Ping Li
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
  State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
  ,
- Pan Zhang
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
  ,
- Tao Wang
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
  ,
- Huaxin Xiao
  Department of Systems Engineering, National University of Defense Technology, Changsha, China
Information Processing and Management: an International Journal, Volume 60, Issue 2•Mar 2023 • https://doi.org/10.1016/j.ipm.2022.103204
Abstract
Describing a long video using multiple sentences, i.e., dense video captioning, is a very challenging task. Existing methods neglect the important fact that the actions of several tempos (a.k.a., frequencies) evolve with the time in ...
Highlights

One Frequency recurrent Transformer with Diversity constraint method for dense video captioning.
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research-article
Prototype contrastive learning for point-supervised temporal action detection
- Ping Li
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
  State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
  ,
- Jiachen Cao
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
  ,
- Xingchao Ye
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
Expert Systems with Applications: An International Journal, Volume 213, Issue PB•Mar 2023 • https://doi.org/10.1016/j.eswa.2022.118965
Abstract
Detecting temporal actions in a video with only single-frame annotation in each action instance or segment, a.k.a., point-level supervision, has emerged as a more challenging task, compared to fully-supervised setting where per-frame annotations ...
Highlights

A pseudo-label generation strategy for point-supervised temporal action detection.
Pseudo labels are efficiently updated to reduce error accumulation during training.
Prototype contrastive constraint leads to more discriminative ...
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research-article
Deep metric learning via group channel-wise ensemble
- Ping Li
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
  State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
  ,
- Guopan Zhao
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
  ,
- Jiajun Chen
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
  ,
- Xianghua Xu
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
Knowledge-Based Systems, Volume 259, Issue C•Jan 2023 • https://doi.org/10.1016/j.knosys.2022.110029
Abstract
Deep metric learning aims at learning the distance metric for data samples by deep neural networks. Essentially, it derives an embedding space where the mappings of semantically related samples are much closer than those of irrelevant ...
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research-article
Coarse-to-fine few-shot classification with deep metric learning
- Ping Li
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
  State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
  ,
- Guopan Zhao
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
  ,
- Xianghua Xu
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
Information Sciences: an International Journal, Volume 610, Issue C•Sep 2022, pp 592-604 • https://doi.org/10.1016/j.ins.2022.08.048
Abstract
Few-shot classification predicts the labels of unseen samples using only a few labeled samples, and employs the samples of the classes disjoint with unseen classes for model training. It faces two primary challenges, i.e., handling ...
6Citation
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research-article
Graph convolutional network meta-learning with multi-granularity POS guidance for video captioning
- Ping Li
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
  ,
- Pan Zhang
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
  ,
- Xianghua Xu
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
Neurocomputing, Volume 472, Issue C•Feb 2022, pp 294-305 • https://doi.org/10.1016/j.neucom.2020.12.137
Abstract
Video as information carrier has gained overwhelming popularity in city surveillance and social networks, such as WeChat, Weibo, and TikTok. To bridge the semantic gap between video content (e.g., user and landmark building) and textual ...
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research-article
RGB-D SLAM in Dynamic Environments Using Point Correlations
- Weichen Dai
  College of Control Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China
  ,
- Yu Zhang
  State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China
  ,
- Ping Li
  State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China
  ,
- Zheng Fang
  Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China
  ,
- Sebastian Scherer
  Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA
IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 44, Issue 1•Jan. 2022, pp 373-389 • https://doi.org/10.1109/TPAMI.2020.3010942
In this paper, a simultaneous localization and mapping (SLAM) method that eliminates the influence of moving objects in dynamic environments is proposed. This method utilizes the correlation between map points to separate points that are part of the ...
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rapid-communication
Deep relational self-Attention networks for scene graph generation
- Ping Li
  Key Laboratory of Complex Systems Modeling and Simulation, School of Computer Science and Technology, Hangzhou Dianzi University, China
  ,
- Zhou Yu
  Key Laboratory of Complex Systems Modeling and Simulation, School of Computer Science and Technology, Hangzhou Dianzi University, China
  ,
- Yibing Zhan
  Key Laboratory of Complex Systems Modeling and Simulation, School of Computer Science and Technology, Hangzhou Dianzi University, China
  JD Explore Academy, Beijing, China
Pattern Recognition Letters, Volume 153, Issue C•Jan 2022, pp 200-206 • https://doi.org/10.1016/j.patrec.2021.12.013
Highlights

We present a relational self attention (RSA) model to jointly model the object and relation contexts.
Abstract
Scene graph generation (SGG) aims to simultaneously detect objects in an image and predict relations for these detected objects. SGG is challenging that requires modeling the contextualized relationships among objects rather than only ...
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correction
Correction to: GP-SLAM: laser-based SLAM approach based on regionalized Gaussian process map reconstruction
- Bo Li
  School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, China
  ,
- Yingqiang Wang
  School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, China
  ,
- Yu Zhang
  State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China
  ,
- Wenjie Zhao
  School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, China
  ,
- Jianyuan Ruan
  School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, China
  ,
- Ping Li
  State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China
Autonomous Robots, Volume 44, Issue 6•Jul 2020, pp 969-969 • https://doi.org/10.1007/s10514-020-09909-w
Unfortunately, the acknowledgement text was incorrectly published in the original article.
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GP-SLAM: laser-based SLAM approach based on regionalized Gaussian process map reconstruction
- Bo Li
  School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, China
  ,
- Yingqiang Wang
  School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, China
  ,
- Yu Zhang
  State Key Labtory of Industrial Control Technology, College of Control Scinece and Engineering, Zhejiang University, Hangzhou, China
  ,
- Wenjie Zhao
  School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, China
  ,
- Jianyuan Ruan
  School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, China
  ,
- Ping Li
  State Key Labtory of Industrial Control Technology, College of Control Scinece and Engineering, Zhejiang University, Hangzhou, China
Autonomous Robots, Volume 44, Issue 6•Jul 2020, pp 947-967 • https://doi.org/10.1007/s10514-020-09906-z
Abstract
Existing laser-based 2D simultaneous localization and mapping (SLAM) methods exhibit limitations with regard to either efficiency or map representation. An ideal method should estimate the map of the environment and the state of the robot quickly ...
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research-article
Vision-Based Formation Control of a Heterogeneous Unmanned System
- Chenzui Li
  College of Control Science and Engineering, Zhejiang University,China
  ,
- Qinyuan Ren
  College of Control Science and Engineering, Zhejiang University,China
  ,
- Fei Chen
  Istituto Italiano di Tecnologia,Department of Advanced Robotics,Italy
  ,
- Ping Li
  College of Control Science and Engineering, Zhejiang University,China
IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society•October 2019, pp 5299-5304• https://doi.org/10.1109/IECON.2019.8927228
A vision-based cooperative formation control method is proposed in this paper for a heterogeneous unmanned system including an UAV (Unmanned Aerial Vehicle) and multiple UGVs (Unmanned Ground Vehicles). Considering the supervisory role of the UAV and the ...
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free
Cycle-SUM: cycle-consistent adversarial LSTM networks for unsupervised video summarization
- Li Yuan
  National University of Singapore
  ,
- Francis EH Tay
  National University of Singapore
  ,
- Ping Li
  Hangzhou Dianzi University
  ,
- Li Zhou
  National University of Singapore
  ,
- Jiashi Feng
  National University of Singapore
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•January 2019, Article No.: 1122, pp 9143-9150• https://doi.org/10.1609/aaai.v33i01.33019143

In this paper, we present a novel unsupervised video summarization model that requires no manual annotation. The proposed model termed Cycle-SUM adopts a new cycle-consistent adversarial LSTM architecture that can effectively maximize the information ...
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research-article
Perceptually Aware Image Retargeting for Mobile Devices
- Yinzuo Zhou
  Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou, China
  ,
- Luming Zhang
  College of Computer Sciences, Zhejiang University, Zhejiang, China
  ,
- Chao Zhang
  Computer Science Department, University of Illinois at Urbana–Champaign, Champaign, IL, USA
  ,
- Ping Li
  School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
  ,
- Xuelong Li
  State Key Laboratory of Transient Optics and Photonics, Center for OPTical IMagery Analysis and Learning, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, China
IEEE Transactions on Image Processing, Volume 27, Issue 5•May 2018, pp 2301-2313 • https://doi.org/10.1109/TIP.2017.2779272

Retargeting aims at adapting an original high-resolution photograph/video to a low-resolution screen with an arbitrary aspect ratio. Conventional approaches are generally based on desktop PCs, since the computation might be intolerable for mobile ...
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research-article
Whole flow field performance prediction by impeller parameters of centrifugal pumps using support vector regression
- Hongying Deng
  Engineering Research Center of Process Equipment and Remanufacturing, Ministry of Education, Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310014, China
  ,
- Yi Liu
  Engineering Research Center of Process Equipment and Remanufacturing, Ministry of Education, Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310014, China
  ,
- Ping Li
  State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou 310027, China
  ,
- Shengchang Zhang
  Engineering Research Center of Process Equipment and Remanufacturing, Ministry of Education, Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310014, China
Advances in Engineering Software, Volume 114, Issue C•December 2017, pp 258-267 • https://doi.org/10.1016/j.advengsoft.2017.07.007

A novel empirical model is proposed to predict the multiple performance indices of the whole flow field using related impeller parameters of centrifugal pumps.The complex nonlinearity relationship between multiple impeller parameters and performance ...
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Article
Online robust low-rank tensor learning
- Ping Li
  School of Computer Science and Technology, Hangzhou Dianzi University and Department of Electrical and Computer Engineering, National University of Singapore
  ,
- Jiashi Feng
  Department of Electrical and Computer Engineering, National University of Singapore
  ,
- Xiaojie Jin
  Department of Electrical and Computer Engineering, National University of Singapore
  ,
- Luming Zhang
  Department of Computer and Information, Hefei University of Technology
  ,
- Xianghua Xu
  School of Computer Science and Technology, Hangzhou Dianzi University
  ,
- Shuicheng Yan
  Qihoo 360 Artificial Intelligence Institute and Department of Electrical and Computer Engineering, National University of Singapore
IJCAI'17: Proceedings of the 26th International Joint Conference on Artificial Intelligence•August 2017, pp 2180-2186

The rapid increase of multidimensional data ( a.k.a. tensor) like videos brings new challenges for low-rank data modeling approaches such as dynamic data size, complex high-order relations, and multiplicity of low-rank structures. Resolving these ...
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research-article
Adjustable preference affinity propagation clustering
- Ping Li
  State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, PR China
  ,
- Haifeng Ji
  State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, PR China
  ,
- Baoliang Wang
  State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, PR China
  ,
- Zhiyao Huang
  State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, PR China
  ,
- Haiqing Li
  State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, PR China
Pattern Recognition Letters, Volume 85, Issue C•January 2017, pp 72-78 • https://doi.org/10.1016/j.patrec.2016.11.017

An Adjustable Preference Affinity Propagation (APAP) algorithm is proposed.The initial value of each element preference pk is determined according to the data distribution.Element preference pk can be automatically adjusted during the iteration ...
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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

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Bridging knowledge distillation gap for few-sample unsupervised semantic segmentation

Efficient Long-Short Temporal Attention network for unsupervised Video Object Segmentation

Fully Transformer-Equipped Architecture for end-to-end Referring Video Object Segmentation

Adversarial Attacks on Video Object Segmentation With Hard Region Discovery

Truncated attention-aware proposal networks with multi-scale dilation for temporal action detection

Time–frequency recurrent transformer with diversity constraint for dense video captioning

Prototype contrastive learning for point-supervised temporal action detection

Deep metric learning via group channel-wise ensemble

Coarse-to-fine few-shot classification with deep metric learning

Graph convolutional network meta-learning with multi-granularity POS guidance for video captioning

RGB-D SLAM in Dynamic Environments Using Point Correlations

Deep relational self-Attention networks for scene graph generation

Correction to: GP-SLAM: laser-based SLAM approach based on regionalized Gaussian process map reconstruction

GP-SLAM: laser-based SLAM approach based on regionalized Gaussian process map reconstruction

Vision-Based Formation Control of a Heterogeneous Unmanned System

Cycle-SUM: cycle-consistent adversarial LSTM networks for unsupervised video summarization

Perceptually Aware Image Retargeting for Mobile Devices

Whole flow field performance prediction by impeller parameters of centrifugal pumps using support vector regression

Online robust low-rank tensor learning

Adjustable preference affinity propagation clustering