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

Skip-Plan: Procedure Planning in Instructional Videos via Condensed Action Space Learning