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[2508.07388] Invert4TVG: A Temporal Video Grounding Framework with Inversion Tasks Preserving Action Understanding Ability
Summary
The paper presents Invert4TVG, a novel framework for Temporal Video Grounding (TVG) that enhances action understanding through inversion tasks, improving accuracy in video segment localization.
Why It Matters
This research addresses limitations in current TVG methods that often fail to accurately recognize actions, which is crucial for applications in video analysis and AI understanding. By integrating inversion tasks, the framework aims to enhance the model's action comprehension, potentially leading to more effective video grounding solutions.
Key Takeaways
- Invert4TVG integrates inversion tasks to improve action understanding in TVG.
- The framework includes tasks like Verb Completion and Action Recognition to enhance model performance.
- Experiments show a 7.1% improvement in accuracy over existing methods on the Charades-STA dataset.
- The approach utilizes reinforcement learning with carefully designed reward functions.
- This work contributes to advancing AI's ability to comprehend and process video content.
Computer Science > Artificial Intelligence arXiv:2508.07388 (cs) [Submitted on 10 Aug 2025 (v1), last revised 13 Feb 2026 (this version, v2)] Title:Invert4TVG: A Temporal Video Grounding Framework with Inversion Tasks Preserving Action Understanding Ability Authors:Zhaoyu Chen, Hongnan Lin, Yongwei Nie, Fei Ma, Xuemiao Xu, Fei Yu, Chengjiang Long View a PDF of the paper titled Invert4TVG: A Temporal Video Grounding Framework with Inversion Tasks Preserving Action Understanding Ability, by Zhaoyu Chen and 6 other authors View PDF HTML (experimental) Abstract:Temporal Video Grounding (TVG) aims to localize video segments corresponding to a given textual query, which often describes human actions. However, we observe that current methods, usually optimizing for high temporal Intersection-over-Union (IoU), frequently struggle to accurately recognize or understand the underlying actions in both the video and query, thus reducing the effectiveness of these methods. To address this, we propose a novel TVG framework that integrates inversion-based TVG as auxiliary objectives to maintain the model's action understanding ability. We introduce three kinds of inversion TVG tasks derived from the original TVG annotations: (1) Verb Completion, predicting masked verbs (actions) in queries given video segments; (2) Action Recognition, identifying query-described actions; and (3) Video Description, generating descriptions containing query-relevant actions given video segments. These invers...
