# Video Captioning via Hierarchical Reinforcement Learning Source: [Video Captioning via Hierarchical Reinforcement Learning]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:59:11.405Z Authors: Xin Wang, Wenhu Chen, Jiawei Wu, Yuan-Fang Wang, William Yang Wang Published: 2017-11-29T22:23:59.000Z ## Abstract Video captioning is the task of automatically generating a textual description of the actions in a video. Although previous work (e.g. sequence-to-sequence model) has shown promising results in abstracting a coarse description of a short video, it is still very challenging to caption a video containing multiple fine-grained actions with a detailed description. This paper aims to address the challenge by proposing a novel hierarchical reinforcement learning framework for video captioning, where a high-level Manager module learns to design sub-goals and a low-level Worker module recognizes the primitive actions to fulfill the sub-goal. With this compositional framework to reinforce video captioning at different levels, our approach significantly outperforms all the baseline methods on a newly introduced large-scale dataset for fine-grained video captioning. Furthermore, our non-ensemble model has already achieved the state-of-the-art results on the widely-used MSR-VTT dataset. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Video Paragraph Generation on Charades Captions](): Video Paragraph Generation; Charades Captions; BLEU-4: 18.8. ## Original sources and implementation [Original paper]() [PDF]()