Ruiz Ovejero, AdriàRudovic, OgnjenBinefa i Valls, XavierPantic, Maja2018-03-022018-03-022017Ruiz 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_110302-9743http://hdl.handle.net/10230/34042Comunicació presentada a: Computer Vision – ACCV 2016, 13th Asian Conference on Computer Vision, celebrat a Taipei, Taiwan, del 20 al 24 de novembre de 2016.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.application/pdfeng© Springer The final publication is available at Springer via https://www.springerprofessional.de/multi-instance-dynamic-ordinal-random-fields-for-weakly-supervis/12130658info:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1007/978-3-319-54184-6_11info:eu-repo/semantics/openAccess