  Superpixel-based Higher-order Conditional Random Fields (CRFs) are effective
in enforcing long-range consistency in pixel-wise labeling problems, such as
semantic segmentation. However, their major short coming is considerably longer
time to learn higher-order potentials and extra hyperparameters and/or weights
compared with pairwise models. This paper proposes a superpixel-enhanced
pairwise CRF framework that consists of the conventional pairwise as well as
our proposed superpixel-enhanced pairwise (SP-Pairwise) potentials. SP-Pairwise
potentials incorporate the superpixel-based higher-order cues by conditioning
on a segment filtered image and share the same set of parameters as the
conventional pairwise potentials. Therefore, the proposed superpixel-enhanced
pairwise CRF has a lower time complexity in parameter learning and at the same
time it outperforms higher-order CRF in terms of inference accuracy. Moreover,
the new scheme takes advantage of the pre-trained pairwise models by reusing
their parameters and/or weights, which provides a significant accuracy boost on
the basis of CRF-RNN even without training. Experiments on MSRC-21 and PASCAL
VOC 2012 dataset confirm the effectiveness of our method.
