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

• URL: http://arxiv.org/abs/2508.07388v1
• Date: Sun, 10 Aug 2025 15:38:04 GMT
• Title: Invert4TVG: A Temporal Video Grounding Framework with Inversion Tasks for Enhanced Action Understanding
• Authors: Zhaoyu Chen, Hongnan Lin, Yongwei Nie, Fei Ma, Xuemiao Xu, Fei Yu, Chengjiang Long,
• Abstract summary: Temporal Video Grounding (TVG) seeks to localize video segments matching a given textual query.<n>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.<n>We introduce Inversion Tasks for TVG (Invert4TVG), a novel framework that enhances both localization accuracy and action understanding without additional data.
• Score: 31.472828313904316
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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