The onset of long-form egocentric datasets such as Ego4D and EPIC-Kitchens presents a new challenge for the task of Temporal Sentence Grounding (TSG). Compared to traditional benchmarks on which this task is evaluated, these datasets offer finer-grained sentences to ground in notably longer videos. In this paper, we develop an approach for learning to ground sentences in these datasets using only narrations and their corresponding rough narration timestamps. We propose to artificially merge clips to train for temporal grounding in a contrastive manner using text-conditioning attention. This Clip Merging (CliMer) approach is shown to be effective when compared with a high performing TSG method -- e.g. mean R@1 improves from 3.9 to 5.7 on Ego4D and from 10.7 to 13.0 on EPIC-Kitchens. Code and data splits available from: https://github.com/keflanagan/CliMer

翻译：长篇自我中心数据集（如Ego4D和EPIC-Kitchens）的出现为时间句子定位任务带来了新挑战。相较于该任务的传统评估基准，这些数据集提供了在显著更长的视频中进行定位的细粒度句子。本文开发了一种仅利用叙述及其对应粗略叙述时间戳来学习在这些数据集中进行句子定位的方法。我们提出通过文本条件注意力机制，以对比学习方式人工合并视频片段以训练时间定位能力。与高性能TSG方法相比，这种片段合并方法被证明是有效的——例如，在Ego4D上平均R@1从3.9提升至5.7，在EPIC-Kitchens上从10.7提升至13.0。代码与数据划分见：https://github.com/keflanagan/CliMer