TimeScope: Towards Task-Oriented Temporal Grounding In Long Videos

2025-09-30

AI Summary

This paper introduces Task-Oriented Temporal Grounding (ToTG), a novel problem that requires precisely localizing key temporal intervals in long videos based on natural language task descriptions. To address ToTG, we construct the first dedicated benchmark—ToTG Bench—and a high-quality dataset—ToTG Pile—and propose a progressive reasoning framework: first performing coarse-grained candidate segment retrieval, then refining start and end timestamps via fine-grained localization. This design significantly improves temporal localization accuracy and generalization for long-video understanding. Extensive experiments demonstrate that our method consistently outperforms existing temporal grounding approaches and state-of-the-art multimodal large language models across diverse evaluation settings. Results validate its effectiveness, robustness, and practical utility, establishing a new paradigm and providing a practical toolkit for long-video comprehension.

Technology Category

• Natural Language Processing: Language Grounding & Multi-modal NLP
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Computer Vision: Video Understanding & Activity Analysis

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Identifying key moments in long videos is essential for downstream understanding and reasoning tasks. In this paper, we introduce a new problem, Taskoriented Temporal Grounding ToTG, which aims to localize time intervals containing the necessary information based on a task's natural description. Along with the definition, we also present ToTG Bench, a comprehensive benchmark for evaluating the performance on ToTG. ToTG is particularly challenging for traditional approaches due to their limited generalizability and difficulty in handling long videos. To address these challenges, we propose TimeScope, a novel framework built upon progressive reasoning. TimeScope first identifies a coarse-grained temporal scope in the long video that likely contains the key moments, and then refines this scope through finegrained moment partitioning. Additionally, we curate a highquality dataset, namely ToTG Pile, to enhance TimeScope's ability to perform progressive temporal grounding effectively. Extensive experiments demonstrate that TimeScope consistently outperforms both existing temporalgrounding methods and popular MLLMs across various settings, highlighting its effectiveness in addressing this new challenging problem.

Problem

• Localizing task-relevant time intervals in long videos
• Addressing limited generalizability of traditional temporal grounding methods
• Handling progressive reasoning challenges in lengthy video content

Innovation

• Progressive reasoning framework for temporal grounding
• Coarse-to-fine temporal scope refinement approach
• High-quality dataset curation for enhanced performance