Intentonomy: A dataset and study towards human intent understanding
Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt
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Intentonomy : A dataset and study towards human intent understanding. / Jia, Menglin; Wu, Zuxuan; Reiter, Austin; Cardie, Claire; Belongie, Serge; Lim, Ser Nam.
I: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2021, s. 12981-12991.Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt
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TY - GEN
T1 - Intentonomy
T2 - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021
AU - Jia, Menglin
AU - Wu, Zuxuan
AU - Reiter, Austin
AU - Cardie, Claire
AU - Belongie, Serge
AU - Lim, Ser Nam
N1 - Funding Information: Acknowledgement We thank Luke Chesser and Timothy Carbone from Unsplash for providing the images, Kimberly Wilber and Bor-chun Chen for tips and suggestions about the annotation interface and annotator management, Kevin Musgrave for the general discussion, and anonymous reviewers for their valuable feedback. This work is supported by a Facebook AI research grant awarded to Cornell University. Publisher Copyright: © 2021 IEEE
PY - 2021
Y1 - 2021
N2 - An image is worth a thousand words, conveying information that goes beyond the mere visual content therein. In this paper, we study the intent behind social media images with an aim to analyze how visual information can facilitate recognition of human intent. Towards this goal, we introduce an intent dataset, Intentonomy, comprising 14K images covering a wide range of everyday scenes. These images are manually annotated with 28 intent categories derived from a social psychology taxonomy. We then systematically study whether, and to what extent, commonly used visual information, i.e., object and context, contribute to human motive understanding. Based on our findings, we conduct further study to quantify the effect of attending to object and context classes as well as textual information in the form of hashtags when training an intent classifier. Our results quantitatively and qualitatively shed light on how visual and textual information can produce observable effects when predicting intent.
AB - An image is worth a thousand words, conveying information that goes beyond the mere visual content therein. In this paper, we study the intent behind social media images with an aim to analyze how visual information can facilitate recognition of human intent. Towards this goal, we introduce an intent dataset, Intentonomy, comprising 14K images covering a wide range of everyday scenes. These images are manually annotated with 28 intent categories derived from a social psychology taxonomy. We then systematically study whether, and to what extent, commonly used visual information, i.e., object and context, contribute to human motive understanding. Based on our findings, we conduct further study to quantify the effect of attending to object and context classes as well as textual information in the form of hashtags when training an intent classifier. Our results quantitatively and qualitatively shed light on how visual and textual information can produce observable effects when predicting intent.
UR - http://www.scopus.com/inward/record.url?scp=85123160639&partnerID=8YFLogxK
U2 - 10.1109/CVPR46437.2021.01279
DO - 10.1109/CVPR46437.2021.01279
M3 - Conference article
AN - SCOPUS:85123160639
SP - 12981
EP - 12991
JO - I E E E Conference on Computer Vision and Pattern Recognition. Proceedings
JF - I E E E Conference on Computer Vision and Pattern Recognition. Proceedings
SN - 1063-6919
Y2 - 19 June 2021 through 25 June 2021
ER -
ID: 301816716