标题： Invert4TVG：一种具有反转任务的时序视频定位框架以增强动作理解 显示英文标题

标题： Invert4TVG: A Temporal Video Grounding Framework with Inversion Tasks for Enhanced Action Understanding

摘要： 时间视频定位（TVG）旨在定位与给定文本查询匹配的视频片段。当前的方法虽然针对高时间交并比（IoU）进行优化，但往往过度拟合这一指标，损害了视频和查询中的语义动作理解，这是稳健TVG的关键因素。为了解决这个问题，我们引入了TVG的反转任务（Invert4TVG），这是一个新颖的框架，在不增加数据的情况下提高了定位精度和动作理解。我们的方法利用了从现有TVG注释中派生的三个反转任务：（1）动词补全，从视频片段中预测查询中被遮罩的动作动词；（2）动作识别，识别查询描述的动作；（3）视频描述，生成明确嵌入查询相关动作的视频片段描述。这些任务通过具有精心设计奖励函数的强化学习框架与TVG集成，确保定位和语义的平衡优化。实验表明，我们的方法优于最先进的方法，在Charades-STA上，对于一个3B模型，R1@0.7的性能提升了7.1%，相比Time-R1。通过将TVG反转以从片段中推导出与查询相关的动作，我们的方法增强了语义理解，显著提高了定位精度的上限。 显示英文摘要

摘要： Temporal Video Grounding (TVG) seeks to localize video segments matching a given textual query. Current methods, while optimizing for high temporal Intersection-over-Union (IoU), often overfit to this metric, compromising semantic action understanding in the video and query, a critical factor for robust TVG. To address this, we introduce Inversion Tasks for TVG (Invert4TVG), a novel framework that enhances both localization accuracy and action understanding without additional data. Our approach leverages three inversion tasks derived from existing TVG annotations: (1) Verb Completion, predicting masked action verbs in queries from video segments; (2) Action Recognition, identifying query-described actions; and (3) Video Description, generating descriptions of video segments that explicitly embed query-relevant actions. These tasks, integrated with TVG via a reinforcement learning framework with well-designed reward functions, ensure balanced optimization of localization and semantics. Experiments show our method outperforms state-of-the-art approaches, achieving a 7.1\% improvement in R1@0.7 on Charades-STA for a 3B model compared to Time-R1. By inverting TVG to derive query-related actions from segments, our approach strengthens semantic understanding, significantly raising the ceiling of localization accuracy.