T2SGrid: Temporal-to-Spatial Gridification for Video Temporal Grounding

2026-03-07

AI Summary

This work addresses the challenges faced by existing video temporal localization methods—such as high computational cost, sparse attention, and loss of spatial detail—in modeling temporal dynamics. The authors propose a novel time-to-space transformation paradigm that restructures a sequence of video frames into a structured 2D grid image using overlapping sliding windows, thereby preserving fine-grained spatial information while explicitly encoding global temporal relationships. By integrating composite textual timestamps with a vision-language foundation model architecture, the approach unifies local attention mechanisms with global temporal awareness. The method achieves state-of-the-art performance across multiple standard video temporal localization benchmarks, significantly improving both localization accuracy and temporal understanding.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

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

Abstract

Video Temporal Grounding (VTG) aims to localize the video segment that corresponds to a natural language query, which requires a comprehensive understanding of complex temporal dynamics. Existing Vision-LMMs typically perceive temporal dynamics via positional encoding, text-based timestamps, or visual frame numbering. However, these approaches exhibit notable limitations: assigning each frame a text-based timestamp token introduces additional computational overhead and leads to sparsity in visual attention, positional encoding struggles to capture absolute temporal information, and visual frame numbering often compromises spatial detail. To address these issues, we propose Temporal to Spatial Gridification (T2SGrid), a novel framework that reformulates video temporal understanding as a spatial understanding task. The core idea of T2SGrid is to process video content in clips rather than individual frames. we employ a overlapping sliding windows mechanism to segment the video into temporal clips. Within each window, frames are arranged chronologically in a row-major order into a composite grid image, effectively transforming temporal sequences into structured 2D layouts. The gridification not only encodes temporal information but also enhances local attention within each grid. Furthermore, T2SGrid enables the use of composite text timestamps to establish global temporal awareness. Experiments on standard VTG benchmarks demonstrate that T2SGrid achieves superior performance.

Problem

• Video Temporal Grounding
• Temporal Dynamics
• Vision-LMMs
• Temporal Understanding
• Attention Mechanism

Innovation

• Temporal-to-Spatial Gridification
• Video Temporal Grounding
• Composite Grid Image
• Sliding Window Mechanism
• Vision-Language Models