Towards One-to-Many Temporal Grounding

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

A new study introduces One-to-Many Temporal Grounding (OMTG), a critical video understanding task requiring localization of multiple disjoint segments for a single query. Traditional Multi-modal Large Language Models (MLLMs) struggle with OMTG due to a lack of event cardinality perception. To address this, researchers established the first comprehensive OMTG benchmark, introducing Count Accuracy (C-Acc) and Effective Temporal F1 (EtF1) metrics. They also curated a 56k-sample OMTG dataset using a sophisticated pipeline and developed novel temporal and Chain-of-Thought-leveraging caption reward functions. Their model, based on Qwen3-VL-4B and trained with a two-stage SFT+RL strategy, achieved a state-of-the-art EtF1 of 43.65% on the OMTG Bench. This performance surpassed Gemini 2.5 Pro and Seed-1.8 by 15.85% and 15.61%, respectively.

Key takeaway

Machine learning engineers developing video understanding systems must recognize that traditional one-to-one temporal grounding models are insufficient for real-world scenarios with recurring events. You should adopt the One-to-Many Temporal Grounding (OMTG) framework, utilizing metrics like Effective Temporal F1 (EtF1) and Count Accuracy (C-Acc) for robust evaluation. Implement a two-stage SFT and RL approach with tailored temporal and caption rewards to enhance multi-segment localization and event counting.

Key insights

One-to-Many Temporal Grounding requires models to perceive event cardinality for localizing multiple video segments corresponding to a single query.

Principles

• One-to-one metrics fail for multi-segment video grounding.
• Event cardinality perception is vital for accurate multi-occurrence localization.
• RL with tailored rewards enhances multi-segment localization and counting.

Method

A two-stage SFT+RL strategy optimizes models using composite rewards: temporal (tIoU, C-Acc), Chain-of-Thought caption quality, and length penalty for precise multi-segment localization.

In practice

• Build OMTG datasets via MLLM-driven event discovery and verification.
• Integrate CoT caption rewards for precise and complete segment localization.
• Apply C-Acc reward to explicitly correct event cardinality perception.

Topics

• Temporal Grounding
• Video Understanding
• Multi-modal LLMs
• Reinforcement Learning
• Dataset Construction
• Evaluation Metrics
• Chain-of-Thought

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

Related on AIssential

• Towards One-to-Many Temporal Grounding — One-to-Many Temporal Grounding (OMTG) addresses localizing multiple video segments for a single query, overcoming MLLM limitations.
• LATERN: Test-Time Context-Aware Explainable Video Anomaly Detection — LATERN enhances VLM-based video anomaly detection by integrating temporal context for coherent explanations.
• Enhancing Visual Token Representations for Video Large Language Models via Training-Free Spatial-Temporal Pooling and Gridding — ST-GridPool enhances Video LLM visual tokens via training-free hierarchical temporal gridding and norm-based spatial pooling for improved video understanding.
• Foresee-to-Ground: From Predictive Temporal Perception to Evidence-Driven Reasoning for Video Temporal Grounding — F2G improves video temporal grounding by decoupling event identification from precise boundary measurement using citable evidence.
• MLLMs Know When Before Speaking: Revealing and Recovering Temporal Grounding via Attention Cues — MLLMs internally know event timings during prefill, but often lose this signal during answer generation, a gap recoverable via attention-guided re-inference.
• Unified Spatio-Temporal Token Scoring for Efficient Video VLMs — STTS unifies spatio-temporal vision token pruning across ViT and LLM for efficient video VLM processing.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.