多样化查询：用于时序句子定位的区域引导 Transformer
Diversifying Query: Region-Guided Transformer for Temporal Sentence Grounding
AI总结：
针对时序句子定位中基于DETR模型产生重叠冗余提议的问题，提出区域引导Transformer（RGTR），采用锚点对作为时刻查询引入区域指导，并设计IoU感知评分头，有效提升预测多样性与质量，在多个数据集上达到SOTA。
摘要：
时序句子定位是一项具有挑战性的任务，旨在定位与语言描述相关的时刻跨度。尽管近期基于 DETR 的模型通过利用多个可学习的时刻查询取得了显著进展，但它们会产生重叠且冗余的提议，导致预测不准确。我们将这一局限性归因于缺乏与任务相关的指导来引导可学习查询服务于特定模式。此外，由可变和开放词汇语言描述产生的复杂解空间使优化变得复杂，使得可学习查询更难自适应地区分彼此。为了解决这一局限性，我们提出了一种用于时序句子定位的区域引导 Transformer（RGTR），它使时刻查询多样化以消除重叠和冗余的预测。RGTR 不使用可学习查询，而是采用一组锚点对作为时刻查询以引入明确的区域指导。每个锚点对负责特定时间区域的时刻预测，这降低了优化难度并确保了最终预测的多样性。此外，我们设计了一个 IoU 感知评分头以提高提议质量。大量实验证明了 RGTR 的有效性，在 QVHighlights、Charades-STA 和 TACoS 数据集上超越了现有最先进的方法。代码可在 https://github.com/TensorsSun/RGTR 获取。
英文摘要：
Temporal sentence grounding is a challenging task that aims to localize the moment spans relevant to a language description. Although recent DETR-based models have achieved notable progress by leveraging multiple learnable moment queries, they suffer from overlapped and redundant proposals, leading to inaccurate predictions. We attribute this limitation to the lack of task-related guidance for the learnable queries to serve a specific mode. Furthermore, the complex solution space generated by variable and open-vocabulary language descriptions complicates optimization, making it harder for learnable queries to distinguish each other adaptively. To tackle this limitation, we present a Region-Guided TRansformer (RGTR) for temporal sentence grounding, which diversifies moment queries to eliminate overlapped and redundant predictions. Instead of using learnable queries, RGTR adopts a set of anchor pairs as moment queries to introduce explicit regional guidance. Each anchor pair takes charge of moment prediction for a specific temporal region, which reduces the optimization difficulty and ensures the diversity of the final predictions. In addition, we design an IoU-aware scoring head to improve proposal quality. Extensive experiments demonstrate the effectiveness of RGTR, outperforming state-of-the-art methods on QVHighlights, Charades-STA and TACoS datasets. Codes are available at https://github.com/TensorsSun/RGTR
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。
