Measure Twice, Cut Once: Grasping Video Structures and Event Semantics with LLMs for Video Temporal Localization

2025-03-12

AI Summary

This work addresses natural language–guided temporal video grounding by challenging the conventional paradigm of explicit timestamp boundary prediction and proposing a semantic-driven alternative. We introduce MeCo, the first framework that eliminates explicit timestamp regression. MeCo leverages a video large language model (Video LLM) to perform structured token generation and semantic grounding, segmenting videos into semantically coherent event segments and transitional segments. It further formulates a query-guided, fine-grained event description modeling task, unifying localization with high-level semantic understanding. By exploiting the Video LLM’s deep structural and semantic representations of video content, MeCo achieves consistent improvements over state-of-the-art boundary-prediction methods across multiple temporal grounding benchmarks. Empirical results demonstrate MeCo’s superior accuracy, robustness, and cross-task generalization—validating the effectiveness of semantic segmentation over boundary regression for temporal video grounding.

Technology Category

• Natural Language Processing: Language Grounding & Multi-modal NLP
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Video Understanding & Activity Analysis

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Localizing user-queried events through natural language is crucial for video understanding models. Recent methods predominantly adapt Video LLMs to generate event boundary timestamps to handle temporal localization tasks, which struggle to leverage LLMs' powerful semantic understanding. In this work, we introduce MeCo, a novel timestamp-free framework that enables video LLMs to fully harness their intrinsic semantic capabilities for temporal localization tasks. Rather than outputting boundary timestamps, MeCo partitions videos into holistic event and transition segments based on the proposed structural token generation and grounding pipeline, derived from video LLMs' temporal structure understanding capability. We further propose a query-focused captioning task that compels the LLM to extract fine-grained, event-specific details, bridging the gap between localization and higher-level semantics and enhancing localization performance. Extensive experiments on diverse temporal localization tasks show that MeCo consistently outperforms boundary-centric methods, underscoring the benefits of a semantic-driven approach for temporal localization with video LLMs.

Problem

• Localizing events in videos using natural language queries.
• Enhancing video LLMs' semantic understanding for temporal localization.
• Improving event localization without relying on boundary timestamps.

Innovation

• Timestamp-free framework for video temporal localization
• Structural token generation and grounding pipeline
• Query-focused captioning for fine-grained event details