Fewer Steps, Better Performance: Efficient Cross-Modal Clip Trimming for Video Moment Retrieval Using Language

2026-05-28 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision, Natural Language Processing · Depth: Expert, quick

Summary

SpotVMR is an efficient approach designed for video moment retrieval (VMR) using language queries, specifically addressing challenges with untrimmed, overlong videos. Traditional VMR methods down-sample videos into fixed-length clips, which can filter out query-related frames, blur target moment boundaries, and introduce cross-modal misalignment, boundary-bias, and reasoning-bias, making them infeasible for real-world long videos. SpotVMR proposes to trim query-relevant clips directly. It operates as a plug-and-play module, enhancing existing VMR methods' efficiency while maintaining retrieval performance. The approach incorporates a novel clip search model to identify promising video regions conditioned on the language query, utilizes low-cost semantic indexing features for contextual search, and employs distillation loss to optimize end-to-end joint training. Its effectiveness is demonstrated through extensive experiments on three challenging datasets.

Key takeaway

For Machine Learning Engineers developing video moment retrieval systems for long, untrimmed videos, you should evaluate SpotVMR. This approach directly trims query-relevant clips, mitigating the boundary-bias and reasoning-bias introduced by traditional fixed-length clip downsampling. Its plug-and-play nature allows for efficient integration into existing VMR methods, potentially improving retrieval performance and addressing cross-modal misalignment without extensive re-architecting. Consider testing its effectiveness on your specific long-form video datasets.

Key insights

SpotVMR efficiently trims query-relevant video clips for VMR, overcoming fixed-length downsampling issues and improving retrieval performance.

Principles

• Targeted clip trimming enhances VMR accuracy.
• Semantic indexing guides query-relevant video search.
• Distillation loss optimizes joint model training.

Method

SpotVMR employs a novel clip search model to identify promising video regions based on language queries. It uses low-cost semantic indexing features for context and applies distillation loss for end-to-end joint training of the clip selector and VMR model.

In practice

• Integrate SpotVMR into existing VMR pipelines.
• Utilize semantic indexing for efficient video search.
• Apply distillation loss for multi-component model optimization.

Topics

• Video Moment Retrieval
• Cross-Modal Learning
• Language-Guided Search
• Clip Trimming
• Semantic Indexing
• Distillation Loss

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.