2026-04-13T00:08:36Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/340422025-12-21T18:04:33Zcom_2072_6col_2072_452952

Multi-instance dynamic ordinal random fields for weakly-supervised pain intensity estimation Ruiz Ovejero, Adrià Rudovic, Ognjen Binefa i Valls, Xavier Pantic, Maja 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. 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. 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. 2018-03-02T17:43:57Z 2018-03-02T17:43:57Z 2017 info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/acceptedVersion 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). info:eu-repo/grantAgreement/EC/H2020/645012 info:eu-repo/grantAgreement/EC/H2020/645094 info:eu-repo/grantAgreement/EC/H2020/688835 © Springer The final publication is available at Springer via https://www.springerprofessional.de/multi-instance-dynamic-ordinal-random-fields-for-weakly-supervis/12130658 info:eu-repo/semantics/openAccess Springer