research-article

User Preference and Performance using Tagging and Browsing for Image Labeling

Authors:

Bruno Fruchard,

Sylvain Malacria,

Stéphane HuotAuthors Info & Claims

CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

Article No.: 358, Pages 1 - 13

https://doi.org/10.1145/3544548.3580926

Published: 19 April 2023 Publication History

Abstract

Visual content must be labeled to facilitate navigation and retrieval, or provide ground truth data for supervised machine learning approaches. The efficiency of labeling techniques is crucial to produce numerous qualitative labels, but existing techniques remain sparsely evaluated. We systematically evaluate the efficiency of tagging and browsing tasks in relation to the number of images displayed, interaction modes, and the image visual complexity. Tagging consists in focusing on a single image to assign multiple labels (image-oriented strategy), and browsing in focusing on a single label to assign to multiple images (label-oriented strategy). In a first experiment, we focus on the nudges inducing participants to adopt one of the strategies (n=18). In a second experiment, we evaluate the efficiency of the strategies (n=24). Results suggest an image-oriented strategy (tagging task) leads to shorter annotation times, especially for complex images, and participants tend to adopt it regardless of the conditions they face.

Supplementary Material

MP4 File (3544548.3580926-talk-video.mp4)

Pre-recorded Video Presentation

Download
124.90 MB

References

[1]

Axel Antoine, Sylvain Malacria, Nicolai Marquardt, and Géry Casiez. 2021. Interaction Illustration Taxonomy: Classification of Styles and Techniques for Visually Representing Interaction Scenarios. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. ACM, Yokohama Japan, 1–22. https://doi.org/10.1145/3411764.3445586

Digital Library

[2]

Caroline Appert, Michel Beaudouin-Lafon, and Wendy E Mackay. 2005. Context matters: Evaluating Interaction Techniques with the CIS Model. In People and Computers XVIII — Design for Life, Sally Fincher, Panos Markopoulos, David Moore, and Roy Ruddle (Eds.). Springer London, London, 279–295. https://doi.org/10.1007/1-84628-062-1_18

[3]

Olivier Aubert and Yannick Prié. 2007. Advene: an open-source framework for integrating and visualising audiovisual metadata. In Proceedings of the 15th international conference on Multimedia - MULTIMEDIA ’07. ACM Press, Augsburg, Germany, 1005. https://doi.org/10.1145/1291233.1291451

Digital Library

[4]

Monya Baker. 2016. Statisticians issue warning over misuse of P values. Nature 531, 7593 (March 2016), 151–151. https://doi.org/10.1038/nature.2016.19503

[5]

Lonni Besançon and Pierre Dragicevic. 2019. The Continued Prevalence of Dichotomous Inferences at CHI. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems(CHI EA ’19). Association for Computing Machinery, New York, NY, USA, 1–11. https://doi.org/10.1145/3290607.3310432 event-place: Glasgow, Scotland Uk.

Digital Library

[6]

Ana Caraban, Evangelos Karapanos, Daniel Gonçalves, and Pedro Campos. 2019. 23 Ways to Nudge: A Review of Technology-Mediated Nudging in Human-Computer Interaction. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, Glasgow Scotland Uk, 1–15. https://doi.org/10.1145/3290605.3300733

Digital Library

[7]

Chia-Ming Chang, Yi He, Xi Yang, Haoran Xie, and Takeo Igarashi. 2022. DualLabel: Secondary Labels for Challenging Image Annotation. In Graphics Interface 2022. https://openreview.net/forum?id=rrBz2lFETzq

[8]

Chia-Ming Chang, Chia-Hsien Lee, and Takeo Igarashi. 2021. Spatial Labeling: Leveraging Spatial Layout for Improving Label Quality in Non-Expert Image Annotation. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. ACM, Yokohama Japan, 1–12. https://doi.org/10.1145/3411764.3445165

Digital Library

[9]

Chia-Ming Chang, Siddharth Deepak Mishra, and Takeo Igarashi. 2019. A Hierarchical Task Assignment for Manual Image Labeling. In 2019 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). IEEE, Memphis, TN, USA, 139–143. https://doi.org/10.1109/VLHCC.2019.8818828

[10]

Cheng-Chieh Chiang. 2013. Interactive tool for image annotation using a semi-supervised and hierarchical approach. Computer Standards & Interfaces 35, 1 (2013), 9.

Digital Library

[11]

Valeriy Chikhman, Valeriya Bondarko, Marina Danilova, Anna Goluzina, and Yuri Shelepin. 2012. Complexity of images: Experimental and computational estimates compared. Perception 41, 6 (2012), 631–647. Publisher: SAGE Publications Sage UK: London, England.

[12]

Geoff Cumming. 2014. The New Statistics: Why and How. Psychological Science 25, 1 (2014), 7–29. https://doi.org/10.1177/0956797613504966 arXiv:https://doi.org/10.1177/0956797613504966PMID: 24220629.

[13]

Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. ImageNet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition. 248–255. https://doi.org/10.1109/CVPR.2009.5206848

[14]

Don C. Donderi. 2006. Visual complexity: A review.Psychological Bulletin 132, 1 (Jan. 2006), 73–97. https://doi.org/10.1037/0033-2909.132.1.73

[15]

Pierre Dragicevic. 2016. Fair Statistical Communication in HCI. In Modern Statistical Methods for HCI, Judy Robertson and Maurits Kaptein (Eds.). Springer International Publishing, Cham, 291–330. https://doi.org/10.1007/978-3-319-26633-6_13 Series Title: Human–Computer Interaction Series.

[16]

Abhishek Dutta and Andrew Zisserman. 2019. The VIA Annotation Software for Images, Audio and Video. In Proceedings of the 27th ACM International Conference on Multimedia. ACM, Nice France, 2276–2279. https://doi.org/10.1145/3343031.3350535

Digital Library

[17]

Jeremy Elson, John R. Douceur, Jon Howell, and Jared Saul. 2007. Asirra: a CAPTCHA that exploits interest-aligned manual image categorization. In Proceedings of the 14th ACM conference on Computer and communications security - CCS ’07. ACM Press, Alexandria, Virginia, USA, 366. https://doi.org/10.1145/1315245.1315291

Digital Library

[18]

Mica R. Endsley. 1995. Measurement of Situation Awareness in Dynamic Systems. Human Factors: The Journal of the Human Factors and Ergonomics Society 37, 1 (March 1995), 65–84. https://doi.org/10.1518/001872095779049499

[19]

Mica R. Endsley. 2015. Situation Awareness Misconceptions and Misunderstandings. Journal of Cognitive Engineering and Decision Making 9, 1 (March 2015), 4–32. https://doi.org/10.1177/1555343415572631

[20]

Mica R Endsley, Betty Bolté, and Debra G Jones. 2003. Designing for situation awareness: An approach to user-centered design. CRC press.

Digital Library

[21]

Carlos Fernandez-Lozano, Adrian Carballal, Penousal Machado, Antonino Santos, and Juan Romero. 2019. Visual complexity modelling based on image features fusion of multiple kernels. PeerJ 7 (July 2019), e7075. https://doi.org/10.7717/peerj.7075

[22]

Niklas Fiedler, Marc Bestmann, and Norman Hendrich. 2019. ImageTagger: An Open Source Online Platform for Collaborative Image Labeling. In RoboCup 2018: Robot World Cup XXII, Dirk Holz, Katie Genter, Maarouf Saad, and Oskar von Stryk (Eds.). Vol. 11374. Springer International Publishing, Cham, 162–169. https://doi.org/10.1007/978-3-030-27544-0_13 Series Title: Lecture Notes in Computer Science.

Digital Library

[23]

Inc Flickr. Last visited in September 2022. Flickr. https://www.flickr.com/.

[24]

OpenJS Foundation. Last visited in August 2022. Node.js. https://nodejs.org/en/.

[25]

Erik Frøkjær, Morten Hertzum, and Kasper Hornbæk. 2000. Measuring Usability: Are Effectiveness, Efficiency, and Satisfaction Really Correlated?. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems(CHI ’00). Association for Computing Machinery, New York, NY, USA, 345–352. https://doi.org/10.1145/332040.332455 event-place: The Hague, The Netherlands.

Digital Library

[26]

Alix Goguey, Julie Wagner, and Géry Casiez. 2015. Quantifying Object-and Command-Oriented Interaction. In IFIP Conference on Human-Computer Interaction. Springer, 231–239.

[27]

Joey Hagedorn, Joshua Hailpern, and Karrie G. Karahalios. 2008. VCode and VData: illustrating a new framework for supporting the video annotation workflow. In Proceedings of the working conference on Advanced visual interfaces - AVI ’08. ACM Press, Napoli, Italy, 317. https://doi.org/10.1145/1385569.1385622

Digital Library

[28]

Christian Halaschek-Wiener, Jennifer Golbeck, Andrew Schain, Michael Grove, Bijan Parsia, and Jim Hendler. 2005. Photostuff-an image annotation tool for the semantic web. In Proceedings of the 4th international semantic web conference. Citeseer, 6–10.

[29]

Allan Hanbury. 2008. A survey of methods for image annotation. Journal of Visual Languages & Computing 19, 5 (Oct. 2008), 617–627. https://doi.org/10.1016/j.jvlc.2008.01.002

Digital Library

[30]

Lisa L Harlow, Stanley A Mulaik, and James H Steiger. 2013. What if there were no significance tests?Psychology Press.

[31]

Alexander G. Hauptmann, Wei-Hao Lin, Rong Yan, Jun Yang, and Ming-Yu Chen. 2006. Extreme video retrieval: joint maximization of human and computer performance. In Proceedings of the 14th annual ACM international conference on Multimedia - MULTIMEDIA ’06. ACM Press, Santa Barbara, CA, USA, 385. https://doi.org/10.1145/1180639.1180721

Digital Library

[32]

Jouni Helske, Satu Helske, Matthew Cooper, Anders Ynnerman, and Lonni Besançon. 2021. Can Visualization Alleviate Dichotomous Thinking? Effects of Visual Representations on the Cliff Effect. IEEE Transactions on Visualization and Computer Graphics 27, 8(2021), 3397–3409. https://doi.org/10.1109/TVCG.2021.3073466

Digital Library

[33]

D. K. Iakovidis, T. Goudas, C. Smailis, and I. Maglogiannis. 2014. Ratsnake: A Versatile Image Annotation Tool with Application to Computer-Aided Diagnosis. The Scientific World Journal 2014 (2014), 1–12. https://doi.org/10.1155/2014/286856

[34]

Transparent Statistics in Human–Computer Interaction Working Group. 2019. Transparent Statistics Guidelines. https://doi.org/10.5281/zenodo.1186169 (Available at https://transparentstats.github.io/guidelines).

[35]

Raymond Klein. 1988. Inhibitory tagging system facilitates visual search. Nature 334, 6181 (Aug. 1988), 430–431. https://doi.org/10.1038/334430a0

[36]

Adriana Kovashka, Olga Russakovsky, Li Fei-Fei, and Kristen Grauman. 2016. Crowdsourcing in Computer Vision. Foundations and Trends® in Computer Graphics and Vision 10, 3(2016), 177–243. https://doi.org/10.1561/0600000071

[37]

Árni Kristjánsson. 2006. Rapid learning in attention shifts: A review. Visual Cognition 13, 3 (Feb. 2006), 324–362. https://doi.org/10.1080/13506280544000039

[38]

Árni Kristjánsson. 2006. Simultaneous priming along multiple feature dimensions in a visual search task. Vision Research 46, 16 (Aug. 2006), 2554–2570. https://doi.org/10.1016/j.visres.2006.01.015

[39]

Martin Krzywinski and Naomi Altman. 2013. Error bars: the meaning of error bars is often misinterpreted, as is the statistical significance of their overlap. Nature methods 10, 10 (2013), 921–923.

[40]

Walter S. Lasecki, Mitchell Gordon, Danai Koutra, Malte F. Jung, Steven P. Dow, and Jeffrey P. Bigham. 2014. Glance: rapidly coding behavioral video with the crowd. In Proceedings of the 27th annual ACM symposium on User interface software and technology. ACM, Honolulu Hawaii USA, 551–562. https://doi.org/10.1145/2642918.2647367

Digital Library

[41]

Bokyung Lee, Michael Lee, Pan Zhang, Alexander Tessier, and Azam Khan. 2019. Semantic Human Activity Annotation Tool Using Skeletonized Surveillance Videos. In Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers (London, United Kingdom) (UbiComp/ISWC ’19 Adjunct). Association for Computing Machinery, New York, NY, USA, 312–315. https://doi.org/10.1145/3341162.3343807

Digital Library

[42]

Dong Liu, Xian-Sheng Hua, Linjun Yang, Meng Wang, and Hong-Jiang Zhang. 2009. Tag Ranking. In Proceedings of the 18th International Conference on World Wide Web(WWW ’09). Association for Computing Machinery, New York, NY, USA, 351–360. https://doi.org/10.1145/1526709.1526757 event-place: Madrid, Spain.

Digital Library

[43]

Penousal Machado, Juan Romero, Marcos Nadal, Antonino Santos, João Correia, and Adrián Carballal. 2015. Computerized measures of visual complexity. Acta Psychologica 160 (Sept. 2015), 43–57. https://doi.org/10.1016/j.actpsy.2015.06.005

[44]

Wendy E. Mackay. 2002. Which Interaction Technique Works When? Floating Palettes, Marking Menus and Toolglasses Support Different Task Strategies. In Proceedings of the Working Conference on Advanced Visual Interfaces(AVI ’02). Association for Computing Machinery, New York, NY, USA, 203–208. https://doi.org/10.1145/1556262.1556294 event-place: Trento, Italy.

Digital Library

[45]

Meta. Last visited in September 2022. Instagram. https://www.instagram.com/.

[46]

Inc Meta Platforms. Last visited in August 2022. React. https://reactjs.org/.

[47]

Andrew Moravcsik. 2014. Transparency: The Revolution in Qualitative Research. PS: Political Science & Politics 47, 1 (2014), 48–53. https://doi.org/10.1017/S1049096513001789

[48]

Hermann J. Müller and Joseph Krummenacher. 2006. Visual search and selective attention. Visual Cognition 14, 4-8 (Aug. 2006), 389–410. https://doi.org/10.1080/13506280500527676

[49]

Bernd Münzer, Andreas Leibetseder, Sabrina Kletz, and Klaus Schoeffmann. 2019. ECAT - Endoscopic Concept Annotation Tool. In MultiMedia Modeling, Ioannis Kompatsiaris, Benoit Huet, Vasileios Mezaris, Cathal Gurrin, Wen-Huang Cheng, and Stefanos Vrochidis (Eds.). Springer International Publishing, Cham, 571–576.

[50]

Jakob Nielsen and Jonathan Levy. 1994. Measuring usability: preference vs. performance. Commun. ACM 37, 4 (1994), 66–75. Publisher: ACM New York, NY, USA.

Digital Library

[51]

Donald A. Norman. 1981. Categorization of action slips.Psychological Review 88, 1 (1981), 1–15. https://doi.org/10.1037/0033-295X.88.1.1 Place: US Publisher: American Psychological Association.

[52]

Oded Nov and Chen Ye. 2010. Why Do People Tag? Motivations for Photo Tagging. Commun. ACM 53, 7 (jul 2010), 128–131. https://doi.org/10.1145/1785414.1785450

Digital Library

[53]

Michael I Posner, Yoav Cohen, and others. 1984. Components of visual orienting. Attention and performance X: Control of language processes 32 (1984), 531–556. Publisher: Hilldale, NJ.

[54]

Ilana Ritov and Jonathan Baron. 1992. Status-quo and omission biases. Journal of Risk and Uncertainty 5, 1 (Feb. 1992). https://doi.org/10.1007/BF00208786

[55]

Bryan C. Russell, Antonio Torralba, Kevin P. Murphy, and William T. Freeman. 2008. LabelMe: A Database and Web-Based Tool for Image Annotation. International Journal of Computer Vision 77, 1-3 (May 2008), 157–173. https://doi.org/10.1007/s11263-007-0090-8

Digital Library

[56]

Carsten Saathoff, Krishna Chandramouli, Werner Bailer, Peter Schallauer, and Raphaël Troncy. 2011. Multimedia Annotation Tools. In Multimedia Semantics, Raphaël Troncy, Benoit Huet, and Simon Schenk (Eds.). John Wiley & Sons, Ltd, Chichester, UK, 223–239. https://doi.org/10.1002/9781119970231.ch13

[57]

Christoph Sager, Christian Janiesch, and Patrick Zschech. 2021. A survey of image labelling for computer vision applications. Journal of Business Analytics 4, 2 (July 2021), 91–110. https://doi.org/10.1080/2573234X.2021.1908861

[58]

Nadine B. Sarter and David D. Woods. 1992. Mode Error in Supervisory Control of Automated Systems. Proceedings of the Human Factors Society Annual Meeting 36, 1 (Oct. 1992), 26–29. https://doi.org/10.1177/154193129203600108

[59]

Nadine B. Sarter and David D. Woods. 1995. How in the World Did We Ever Get into That Mode? Mode Error and Awareness in Supervisory Control. Human Factors: The Journal of the Human Factors and Ergonomics Society 37, 1 (March 1995), 5–19. https://doi.org/10.1518/001872095779049516

[60]

Alexander Sorokin and David Forsyth. 2008. Utility data annotation with Amazon Mechanical Turk. In 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. IEEE, Anchorage, AK, USA, 1–8. https://doi.org/10.1109/CVPRW.2008.4562953

[61]

Alberto Soto, Oleguer Camerino, Xavier Iglesias, M. Teresa Anguera, and Marta Castañer. 2019. LINCE PLUS: Research Software for Behavior Video Analysis. Apunts Educació Física i Esports137 (July 2019), 149–153. https://doi.org/10.5672/apunts.2014-0983.es.(2019/3).137.11

[62]

Bongwon Suh and Benjamin B Bederson. 2004. Semi-Automatic Image Annotation Using Event and Torso Identification. (2004), 4.

[63]

Yuji Takeda and Akihiro Yagi. 2000. Inhibitory tagging in visual search can be found if search stimuli remain visible. Perception & Psychophysics 62, 5 (July 2000), 927–934. https://doi.org/10.3758/BF03212078

[64]

[64] boot package (R programming language).Last visited in September 2022. https://www.rdocumentation.org/packages/boot/versions/1.3-28.

[65]

[65] stats package (R programming language).Last visited in August 2022. https://rdocumentation.org/packages/stats/versions/3.6.2.

[66]

Richard H. Thaler and Cass R. Sunstein. 2008. Nudge: improving decisions about health, wealth, and happiness. Yale University Press, New Haven. OCLC: ocn181517463.

[67]

Hsing-Lin Tsai, Cheng-Hsien Han, En-Hsin Wu, Chi-Lan Yang, and Hao-Chuan Wang. 2015. Tag & Link: Supporting Regional and Relational Tagging in Images with Direct Annotation. In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems. ACM, Seoul Republic of Korea, 1791–1796. https://doi.org/10.1145/2702613.2732857

Digital Library

[68]

Timo Volkmer, John R. Smith, and Apostol (Paul) Natsev. 2005. A web-based system for collaborative annotation of large image and video collections: an evaluation and user study. In Proceedings of the 13th annual ACM international conference on Multimedia - MULTIMEDIA ’05. ACM Press, Hilton, Singapore, 892. https://doi.org/10.1145/1101149.1101341

Digital Library

[69]

Luis von Ahn and Laura Dabbish. 2004. Labeling images with a computer game. In Proceedings of the 2004 conference on Human factors in computing systems - CHI ’04. ACM Press, Vienna, Austria, 319–326. https://doi.org/10.1145/985692.985733

Digital Library

[70]

Meng Wang, Bingbing Ni, Xian-Sheng Hua, and Tat-Seng Chua. 2012. Assistive tagging: A survey of multimedia tagging with human-computer joint exploration. Comput. Surveys 44, 4 (Aug. 2012), 1–24. https://doi.org/10.1145/2333112.2333120

Digital Library

[71]

Liu Wenyin, Susan T Dumais, Yanfeng Sun, HongJiang Zhang, Mary Czerwinski, Brent A Field, 2001. Semi-Automatic Image Annotation. In Interact, Vol. 1. Citeseer, 326–333.

[72]

Christopher D. Wickens. 2008. Situation Awareness: Review of Mica Endsley’s 1995 Articles on Situation Awareness Theory and Measurement. Human Factors: The Journal of the Human Factors and Ergonomics Society 50, 3 (June 2008), 397–403. https://doi.org/10.1518/001872008X288420

[73]

Jeremy M. Wolfe, George A. Alvarez, Ruth Rosenholtz, Yoana I. Kuzmova, and Ashley M. Sherman. 2011. Visual search for arbitrary objects in real scenes. Attention, Perception, & Psychophysics 73, 6 (Aug. 2011), 1650–1671. https://doi.org/10.3758/s13414-011-0153-3

[74]

Jeremy M. Wolfe and Todd S. Horowitz. 2017. Five factors that guide attention in visual search. Nature Human Behaviour 1, 3 (March 2017), 0058. https://doi.org/10.1038/s41562-017-0058

[75]

Rong Yan, Apostol Natsev, and Murray Campbell. 2008. A learning-based hybrid tagging and browsing approach for efficient manual image annotation. In 2008 IEEE Conference on Computer Vision and Pattern Recognition. 1–8. https://doi.org/10.1109/CVPR.2008.4587380 ISSN: 1063-6919.

Cited By

Zhang KCao ZZheng XBillinghurst M(2024)Identifying Hand-based Input Preference Based on Wearable EEGProceedings of the Augmented Humans International Conference 202410.1145/3652920.3653028(102-118)Online publication date: 4-Apr-2024
https://dl.acm.org/doi/10.1145/3652920.3653028
Atzenbeck C(2024)Unwinding AI's Moral Maze: Hypertext's Ethical PotentialProceedings of the 35th ACM Conference on Hypertext and Social Media10.1145/3648188.3678213(23-28)Online publication date: 10-Sep-2024
https://dl.acm.org/doi/10.1145/3648188.3678213
Wyss AMorgenshtern GHirsch-Hüsler ABernard J(2024)DaedalusData: Exploration, Knowledge Externalization and Labeling of Particles in Medical Manufacturing — A Design StudyIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345632931:1(54-64)Online publication date: 23-Sep-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3456329
Show More Cited By

Index Terms

User Preference and Performance using Tagging and Browsing for Image Labeling
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI
    2. Interaction techniques

Recommendations

Automatic online labeling images via co-active-learning
ICIMCS '09: Proceedings of the First International Conference on Internet Multimedia Computing and Service

The well-built dataset is a pre-requisite for computer vision research. However, the process of collecting and labeling the images is laborious and monotonous. In this paper, we aim to automatic labeling and collecting the images for the visual object ...
Active tagging for image indexing
ICME'09: Proceedings of the 2009 IEEE international conference on Multimedia and Expo

Concept labeling and ontology-free tagging are the two typical manners of image annotation. Despite extensive research efforts have been dedicated to labeling, currently automatic image labeling algorithms are still far from satisfactory, and meanwhile ...
An efficient manual image annotation approach based on tagging and browsing
MS '07: Workshop on multimedia information retrieval on The many faces of multimedia semantics

This paper investigates new approaches to improve the efficiency of manual image annotation and help users to produce better annotation results in a given amount of time. Although important in practice, this issue has rarely been studied in a ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

April 2023

14911 pages

ISBN:9781450394215

DOI:10.1145/3544548

Editors:
Albrecht Schmidt
LMU Munich, Germany60028717
,
Kaisa Väänänen
Tampere University, Finland60011170
,
Tesh Goyal
Google Research, USA60006191
,
Per Ola Kristensson
University of Cambridge, UK60031101
,
Anicia Peters
University of Namibia, Namibia60072704
,
Stefanie Mueller
Massachusetts Institute of Technology, USA60022195
,
Julie R. Williamson
University of Glasgow, UK60001490
,
Max L. Wilson
University of Nottingham, UK60015138

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 April 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

Agence Nationale de la Recherche

Conference

CHI '23

Sponsor:

SIGCHI

CHI '23: CHI Conference on Human Factors in Computing Systems

April 23 - 28, 2023

Hamburg, Germany

Acceptance Rates

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
501
Total Downloads

Downloads (Last 12 months)276
Downloads (Last 6 weeks)14

Reflects downloads up to 15 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zhang KCao ZZheng XBillinghurst M(2024)Identifying Hand-based Input Preference Based on Wearable EEGProceedings of the Augmented Humans International Conference 202410.1145/3652920.3653028(102-118)Online publication date: 4-Apr-2024
https://dl.acm.org/doi/10.1145/3652920.3653028
Atzenbeck C(2024)Unwinding AI's Moral Maze: Hypertext's Ethical PotentialProceedings of the 35th ACM Conference on Hypertext and Social Media10.1145/3648188.3678213(23-28)Online publication date: 10-Sep-2024
https://dl.acm.org/doi/10.1145/3648188.3678213
Wyss AMorgenshtern GHirsch-Hüsler ABernard J(2024)DaedalusData: Exploration, Knowledge Externalization and Labeling of Particles in Medical Manufacturing — A Design StudyIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345632931:1(54-64)Online publication date: 23-Sep-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3456329
Haliburton LLeusmann JWelsch RGhebremedhin SIsaakidis PSchmidt AMayer S(2024)Uncovering labeler bias in machine learning annotation tasksAI and Ethics10.1007/s43681-024-00572-wOnline publication date: 16-Sep-2024
https://doi.org/10.1007/s43681-024-00572-w

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View full text|Download PDF

View Table of Conten