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Updated: Jun 26, 2026
08:06
Eye Movement Monitoring of Memory
Published on: August 15, 2010
An Episode Memory-Guided Dual-Stage Framework for Long-Form Video Temporal Grounding
Summary
This study introduces an episode memory-prompted (EMP) framework for efficient video temporal grounding in long-form videos. The EMP method enhances performance and reduces computational costs for localizing video moments relevant to natural language queries.
Area of Science:
- Computer Science
- Artificial Intelligence
- Machine Learning
Background:
- Video temporal grounding (VTG) aims to identify specific video segments matching natural language queries.
- Existing VTG methods struggle with long-form videos due to inefficient proposal generation strategies.
- There is a need for improved VTG techniques for lengthy video content.
Purpose of the Study:
- To propose an efficient and effective framework for video temporal grounding in long-form videos.
- To address the limitations of existing sliding window and anchor-based methods.
- To develop a method that reduces computational complexity while maintaining performance.
Main Methods:
- Introduced a two-stage episode memory-prompted (EMP) framework.
- Stage 1: Generated dynamic episode memories summarizing video activities using unsupervised learning with discriminability and diversity constraints.
- Stage 2: Utilized augmented memory prompts with frame-level content and language queries for efficient boundary regression.
Main Results:
- The EMP framework demonstrated comparable performance to existing methods on MAD and Ego4d datasets.
- Achieved significant reductions in trainable parameters (over 8.5%) and FLOPs (over 13.9%).
- Validated the efficiency and effectiveness of the proposed memory-prompted approach for long-form VTG.
Conclusions:
- The EMP framework offers an efficient solution for video temporal grounding in long-form videos.
- Unsupervised memory learning and memory prompts effectively guide the localization process.
- The proposed method balances performance and computational efficiency, advancing VTG research for lengthy videos.
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