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dc.contributor.authorLuo, Cheng
dc.contributor.authorSong, Siyang
dc.contributor.authorXie, Weicheng
dc.contributor.authorShen, Linlin
dc.contributor.authorGunes, Hatice
dc.date.accessioned2022-05-12T23:30:34Z
dc.date.available2022-05-12T23:30:34Z
dc.date.issued2022-05-02
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/337105
dc.description.abstractThe activations of Facial Action Units (AUs) mutually influence one another. While the relationship between a pair of AUs can be complex and unique, existing approaches fail to specifically and explicitly represent such cues for each pair of AUs in each facial display. This paper proposes an AU relationship modelling approach that deep learns a unique graph to explicitly describe the relationship between each pair of AUs of the target facial display. Our approach first encodes each AU's activation status and its association with other AUs into a node feature. Then, it learns a pair of multi-dimensional edge features to describe multiple task-specific relationship cues between each pair of AUs. During both node and edge feature learning, our approach also considers the influence of the unique facial display on AUs' relationship by taking the full face representation as an input. Experimental results on BP4D and DISFA datasets show that both node and edge feature learning modules provide large performance improvements for CNN and transformer-based backbones, with our best systems achieving the state-of-the-art AU recognition results. Our approach not only has a strong capability in modelling relationship cues for AU recognition but also can be easily incorporated into various backbones. Our PyTorch code is made available.
dc.publisherInternational Joint Conferences on Artificial Intelligence Organization
dc.rightsAll Rights Reserved
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserved
dc.titleLearning Multi-dimensional Edge Feature-based AU Relation Graph for Facial Action Unit Recognition
dc.typeConference Object
dc.publisher.departmentDepartment of Computer Science And Technology
dc.date.updated2022-05-11T15:48:04Z
prism.publicationNameProceedings of the Thirty-First International Joint Conference on Artificial Intelligence
dc.identifier.doi10.17863/CAM.84524
dcterms.dateAccepted2022-04-20
rioxxterms.versionofrecord10.24963/ijcai.2022/173
rioxxterms.versionAM
dc.contributor.orcidGunes, Hatice [0000-0003-2407-3012]
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/R030782/1)
pubs.funder-project-idEuropean Commission Horizon 2020 (H2020) Societal Challenges (826232)
pubs.conference-nameThirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}
pubs.conference-start-date2022-07-23
cam.orpheus.successMon Aug 08 07:23:16 BST 2022 - Embargo updated
cam.orpheus.counter8
cam.depositDate2022-05-11
pubs.conference-finish-date2022-07-29
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
rioxxterms.freetoread.startdate2023-07-01


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