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00925njm 22002777a 4500 dc Ruiz Ovejero, Adrià author Rudovic, Ognjen author Binefa i Valls, Xavier author Pantic, Maja author 2018-03-02T17:43:57Z 2018-03-02T17:43:57Z 2017 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. Multi-instance dynamic ordinal random fields for weakly-supervised pain intensity estimation