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arXiv · 1609.01465

Multi-instance Dynamic Ordinal Random Fields for Weakly-Supervised Pain Intensity Estimation

Adria Ruiz·Ognjen Rudovic·Xavier Binefa·Maja Pantic
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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.

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Adria Ruiz, Ognjen Rudovic, Xavier Binefa, Maja Pantic. 2016-09-06. Multi-instance Dynamic Ordinal Random Fields for Weakly-Supervised Pain Intensity Estimation. https://arxiv.org/abs/1609.01465

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