  Temporally language grounding in untrimmed videos is a newly-raised task in
video understanding. Most of the existing methods suffer from inferior
efficiency, lacking interpretability, and deviating from the human perception
mechanism. Inspired by human's coarse-to-fine decision-making paradigm, we
formulate a novel Tree-Structured Policy based Progressive Reinforcement
Learning (TSP-PRL) framework to sequentially regulate the temporal boundary by
an iterative refinement process. The semantic concepts are explicitly
represented as the branches in the policy, which contributes to efficiently
decomposing complex policies into an interpretable primitive action.
Progressive reinforcement learning provides correct credit assignment via two
task-oriented rewards that encourage mutual promotion within the
tree-structured policy. We extensively evaluate TSP-PRL on the Charades-STA and
ActivityNet datasets, and experimental results show that TSP-PRL achieves
competitive performance over existing state-of-the-art methods.
