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.

中文翻译：

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基于树结构策略的渐进强化学习，用于视频中的时间语言基础

未修剪视频中的时间语言基础是视频理解中一项新提出的任务。现有的方法大多效率低下，缺乏可解释性，偏离人类感知机制。受人类从粗到细的决策范式的启发，我们制定了一种新的基于树结构策略的渐进强化学习 (TSP-PRL) 框架，通过迭代细化过程顺序调节时间边界。语义概念被明确表示为策略中的分支，这有助于将复杂的策略有效地分解为可解释的原始动作。渐进式强化学习通过两个以任务为导向的奖励提供正确的学分分配，鼓励在树结构策略内相互促进。