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Interactive Video Corpus Moment Retrieval Using Reinforcement Learning

PROCEEDINGS OF THE 30TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA, MM 2022(2022)

Cited 1|Views63
Abstract
Known-item video search is effective with human-in-the-loop to interactively investigate the search result and refine the initial query. Nevertheless, when the first few pages of results are swamped with visually similar items, or the search target is hidden deep in the ranked list, finding the know-item target usually requires a long duration of browsing and result inspection. This paper tackles the problem by reinforcement learning, aiming to reach a search target within a few rounds of interaction by long-term learning from user feedbacks. Specifically, the system interactively plans for navigation path based on feedback and recommends a potential target that maximizes the long-term reward for user comment. We conduct experiments for the challenging task of video corpus moment retrieval (VCMR) to localize moments from a large video corpus. The experimental results on TVR and DiDeMo datasets verify that our proposed work is effective in retrieving the moments that are hidden deep inside the ranked lists of CONQUER and HERO, which are the state-of-the-art auto-search engines for VCMR.
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Key words
Interactive search,video corpus moment retrieval,reinforcement learning,user simulation
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Chat Paper

要点】:本论文通过强化学习的方法解决了知识项视频检索中的困难问题,即当搜索结果的前几页存在大量视觉相似的项或搜索目标位于排名列表的深处时,需要长时间浏览和检查结果才能找到目标。

方法】:利用强化学习,该系统根据用户反馈交互性地规划导航路径,并推荐可能的目标,以最大化用户评论的长期奖励。

实验】:本研究对视频语料库矩检索(VCMR)进行了实验,该任务要求从大规模视频语料库中定位特定时刻。在TVR和DiDeMo数据集上的实验结果验证了我们提出的方法在从CONQUER和HERO这两个VCMR的最新自动搜索引擎的排名列表的深处检索时的有效性。