1. Introduce discrimination into GPDM (D-GPDM), the classification is based on CRF

2. Introduce discrimination into GPDM (D-GPDM), the classification is take care of by based on MIL. The tem In MIL, we use the learned latent point as the feature,
   and we learn a set of instance prototypes. The MIL learning is based on Diverse Density.

3. Using GPDM to learn the latent space trajectory, then classification is done by matching trajectory in the latent space via method in [1]

4. Introduce discrination into GPDM, the classification is taken care of by Gaussian Process classification. But it is hard to really innovate on the
Gaussian Process Classification part. The GPC inference involves approximation approaches like (loopy) belief propagation, expectation propagation,
variational approximations, or Monte Carlo Sampling.

[1] M. Black and A. Jepson. A probabilistic framework for matching temporal trajectories:Condensation-based recognition of
gestures and expressions. In ECCV, 1998.