长度重要：用于时间语句定位的长度感知Transformer
Length Matters: Length-Aware Transformer for Temporal Sentence Grounding
AI总结：
针对时间语句定位中查询角色重叠导致冗余预测的问题，提出长度感知Transformer（LATR），按时间长度分组查询并引入长度分类任务，在三个基准上达到最先进性能。
摘要：
时间语句定位（TSG）是一项极具挑战性的任务，旨在未修剪的视频中定位与给定自然语言描述相对应的时间片段。得益于可学习查询的设计，基于DETR的模型在TSG任务中取得了显著进展。然而，缺乏显式监督常常导致学习到的查询在角色上重叠，从而产生冗余预测。因此，我们提出通过利用视频-描述对的长度的先验信息，使每个查询履行其指定角色，从而改进TSG。在本文中，我们介绍了用于TSG的长度感知Transformer（LATR），它分配不同的查询来处理基于不同时间长度的预测。具体来说，我们将所有查询分为三组，分别负责短、中、长时间跨度的片段。在训练期间，引入了一个额外的长度分类任务。来自长度不匹配查询的预测被抑制，引导每个查询专门处理其指定功能。大量实验证明了我们LATR的有效性，在三个公开基准上实现了最先进的性能。此外，消融研究验证了我们方法每个组件的贡献以及将长度先验纳入TSG任务的关键作用。
英文摘要：
Temporal sentence grounding (TSG) is a highly challenging task aiming to localize the temporal segment within an untrimmed video corresponding to a given natural language description. Benefiting from the design of learnable queries, the DETR-based models have achieved substantial advancements in the TSG task. However, the absence of explicit supervision often causes the learned queries to overlap in roles, leading to redundant predictions. Therefore, we propose to improve TSG by making each query fulfill its designated role, leveraging the length priors of the video-description pairs. In this paper, we introduce the Length-Aware Transformer (LATR) for TSG, which assigns different queries to handle predictions based on varying temporal lengths. Specifically, we divide all queries into three groups, responsible for segments with short, middle, and long temporal durations, respectively. During training, an additional length classification task is introduced. Predictions from queries with mismatched lengths are suppressed, guiding each query to specialize in its designated function. Extensive experiments demonstrate the effectiveness of our LATR, achieving state-of-the-art performance on three public benchmarks. Furthermore, the ablation studies validate the contribution of each component of our method and the critical role of incorporating length priors into the TSG task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
