  We consider the problem of minimizing a function represented as a sum of
submodular terms. We assume each term allows an efficient computation of {\em
exchange capacities}. This holds, for example, for terms depending on a small
number of variables, or for certain cardinality-dependent terms.
  A naive application of submodular minimization algorithms would not exploit
the existence of specialized exchange capacity subroutines for individual
terms. To overcome this, we cast the problem as a {\em submodular flow} (SF)
problem in an auxiliary graph, and show that applying most existing SF
algorithms would rely only on these subroutines.
  We then explore in more detail Iwata's capacity scaling approach for
submodular flows (Math. Programming, 76(2):299--308, 1997). In particular, we
show how to improve its complexity in the case when the function contains
cardinality-dependent terms.
