Towards One-to-Many Temporal Grounding

2026-06-04 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, quick

Summary

One-to-Many Temporal Grounding (OMTG) addresses the challenge of localizing multiple disjoint video segments corresponding to a single textual query, a common real-world scenario where prior single-segment retrieval methods and state-of-the-art MLLMs often fail due to a lack of event cardinality perception. A new systematic solution introduces the first comprehensive OMTG benchmark, featuring Count Accuracy (C-Acc) and Effective Temporal F1 (EtF1) metrics. This solution also curates a high-quality OMTG dataset of 56k samples and develops novel temporal and caption reward functions, with the latter leveraging Chain-of-Thought reasoning over dense video captions for policy optimization. The model achieves a new state-of-the-art EtF1 of 43.65% on OMTG Bench, surpassing Gemini 2.5 Pro by 15.85% and Seed-1.8 by 15.61%.

Key takeaway

For Machine Learning Engineers developing video understanding systems, if your application requires localizing multiple disjoint video segments for a single query, recognize that traditional one-to-one temporal grounding models are insufficient. You should explore One-to-Many Temporal Grounding (OMTG) benchmarks and consider implementing specialized reward functions, particularly those leveraging Chain-of-Thought reasoning over dense video captions, to significantly improve model performance and address real-world complexity.

Key insights

One-to-Many Temporal Grounding (OMTG) addresses localizing multiple video segments for a single query, overcoming MLLM limitations.

Principles

• Multi-segment video grounding requires event cardinality perception.
• Reward functions can guide policy optimization for preciseness and completeness.

Method

Policy optimization is guided by novel temporal and Chain-of-Thought caption reward functions, leveraging dense video captions for explicit guidance.

In practice

• Evaluate video grounding models using OMTG benchmarks with C-Acc and EtF1.
• Develop reward functions incorporating Chain-of-Thought for multi-segment tasks.

Topics

• Temporal Grounding
• Video Localization
• Multimodal Large Language Models
• Chain-of-Thought Reasoning
• Reward Functions
• Video Datasets

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

Related on AIssential

• UniversalVTG: A Universal and Lightweight Foundation Model for Video Temporal Grounding — UniversalVTG offers a lightweight, cross-dataset pretraining approach for video temporal grounding, outperforming larger MLLMs.
• Chain-of-Glimpse: Search-Guided Progressive Object-Grounded Reasoning for Video Understanding — Chain-of-Glimpse improves video understanding by grounding reasoning steps to specific visual objects via a search-guided, RL-optimized controller.
• Towards One-to-Many Temporal Grounding — One-to-Many Temporal Grounding requires models to perceive event cardinality for localizing multiple video segments corresponding to a single query.
• Flat-Pack Bench: Evaluating Spatio-Temporal Understanding in Large Vision-Language Models through Furniture Assembly — Current LVLMs significantly struggle with fine-grained spatio-temporal reasoning and object interaction in complex video scenarios.
• \textsc{CR-Seg}: Attention-Guided and CoT-Enhanced Coarse-to-Refined Reasoning Segmentation — CR-Seg integrates attention-guided localization with Chain-of-Thought reasoning to refine segmentation masks from complex language descriptions.
• Denoise and Align: Diffusion-Driven Foreground Knowledge Prompting for Open-Vocabulary Temporal Action Detection — DFAlign uses diffusion-based denoising to generate foreground knowledge, improving open-vocabulary temporal action detection.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.