Tree-Structured Policy based Progressive Reinforcement Learning for Temporally Language Grounding in Video
This addresses the task of video understanding for applications like video retrieval, but it is incremental as it builds on existing reinforcement learning methods with a novel tree-structured approach.
The paper tackles the problem of temporally grounding language in untrimmed videos by proposing a Tree-Structured Policy based Progressive Reinforcement Learning (TSP-PRL) framework, which achieves competitive performance on Charades-STA and ActivityNet datasets.
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.