  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.
