Multi-instance dynamic ordinal random fields for weakly-supervised pain intensity estimation

Ruiz Ovejero, Adrià; Rudovic, Ognjen; Binefa i Valls, Xavier; Pantic, Maja

Multi-instance dynamic ordinal random fields for weakly-supervised pain intensity estimation

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dc.contributor.author Ruiz Ovejero, Adriàca
dc.contributor.author Rudovic, Ognjenca
dc.contributor.author Binefa i Valls, Xavierca
dc.contributor.author Pantic, Majaca
dc.date.accessioned 2018-03-02T17:43:57Z
dc.date.available 2018-03-02T17:43:57Z
dc.date.issued 2017
dc.description Comunicació presentada a: Computer Vision – ACCV 2016, 13th Asian Conference on Computer Vision, celebrat a Taipei, Taiwan, del 20 al 24 de novembre de 2016.ca
dc.description.abstract In this paper, we address the Multi-Instance-Learning (MIL) problem when bag labels are naturally represented as ordinal variables (Multi-Instance-Ordinal Regression). Moreover, we consider the case where bags are temporal sequences of ordinal instances. To model this, we propose the novel Multi-Instance Dynamic Ordinal Random Fields (MI-DORF). In this model, we treat instance-labels inside the bag as latent ordinal states. The MIL assumption is modelled by incorporating a high-order cardinality potential relating bag and instance-labels, into the energy function. We show the benefits of the proposed approach on the task of weakly-supervised pain intensity estimation from the UNBC Shoulder-Pain Database. In our experiments, the proposed approach significantly outperforms alternative non-ordinal methods that either ignore the MIL assumption, or do not model dynamic information in target data.en
dc.description.sponsorship This paper is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grants agreement no. 645012 (KRISTINA), no. 645094 (SEWA) and no. 688835 (DE-ENIGMA). Adria Ruiz would also like to acknowledge Spanish Government to provide support under grant FPU13/01740.
dc.format.mimetype application/pdf
dc.identifier.citation Ruiz A, Rudovic O, Binefa X, Pantic M. Multi-instance dynamic ordinal random fields for weakly-supervised pain intensity estimation. In: Lai SH, Lepetit V, Nishino K, Sato Y. Computer Vision – ACCV 2016. 13th Asian Conference on Computer Vision, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part II. [Cham]: Springer International Publishing, 2017. [17] p. (LNCS; no. 10112). DOI: 10.1007/978-3-319-54184-6_11
dc.identifier.doi http://dx.doi.org/10.1007/978-3-319-54184-6_11
dc.identifier.issn 0302-9743
dc.identifier.uri http://hdl.handle.net/10230/34042
dc.language.iso eng
dc.publisher Springerca
dc.relation.ispartof Lai SH, Lepetit V, Nishino K, Sato Y. Computer Vision – ACCV 2016. 13th Asian Conference on Computer Vision, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part II. [Cham]: Springer International Publishing, 2017. [17] p. (LNCS; no. 10112).
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/645012
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/645094
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/688835
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.title Multi-instance dynamic ordinal random fields for weakly-supervised pain intensity estimationca
dc.type info:eu-repo/semantics/conferenceObject
dc.type.version info:eu-repo/semantics/acceptedVersion

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