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

arXiv: https://arxiv.org/abs/1609.01465 · HTML: https://mlsanity.com/paper/1609.01465/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2016.

## Built on by (most cited first, 2 of 2)

- [Weakly Supervised Learning for Facial Affective Behavior Analysis : A Review](https://mlsanity.com/paper/2101.09858/) (2021)
- [Appearance-Invariant Detection of Suggestive Motion via Laban Movement Descriptors](https://mlsanity.com/paper/2605.24488/) (2026)

## Abstract (arXiv)

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.