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

TL;DR

This paper introduces MI-DORF, a novel model for weakly-supervised pain intensity estimation that effectively captures ordinal, temporal, and multi-instance information, outperforming non-ordinal approaches.

Contribution

The paper proposes MI-DORF, a new model for multi-instance ordinal regression with temporal sequences, integrating high-order potentials to improve pain intensity estimation.

Findings

Significant improvement over non-ordinal methods

Effective modeling of ordinal and temporal data

Superior performance on UNBC Shoulder-Pain Database

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.