  In this paper, we propose Skip-Plan, a condensed action space learning method
for procedure planning in instructional videos. Current procedure planning
methods all stick to the state-action pair prediction at every timestep and
generate actions adjacently. Although it coincides with human intuition, such a
methodology consistently struggles with high-dimensional state supervision and
error accumulation on action sequences. In this work, we abstract the procedure
planning problem as a mathematical chain model. By skipping uncertain nodes and
edges in action chains, we transfer long and complex sequence functions into
short but reliable ones in two ways. First, we skip all the intermediate state
supervision and only focus on action predictions. Second, we decompose
relatively long chains into multiple short sub-chains by skipping unreliable
intermediate actions. By this means, our model explores all sorts of reliable
sub-relations within an action sequence in the condensed action space.
Extensive experiments show Skip-Plan achieves state-of-the-art performance on
the CrossTask and COIN benchmarks for procedure planning.
