# Universal Video Temporal Grounding with Generative Multi-modal Large Language Models

arXiv: https://arxiv.org/abs/2506.18883 · HTML: https://mlsanity.com/paper/2506.18883/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Placed in Video understanding with MLLMs, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 59 of its 73 references that are in the map.
- First posted on arXiv 23 Jun 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Video understanding with MLLMs](https://mlsanity.com/mllm/video-understanding-with-mllms/)

## Builds on (most cited first, 30 of 59)

- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://mlsanity.com/paper/2412.05271/) (2024)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding](https://mlsanity.com/paper/2306.02858/) (2023)
- [Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models](https://mlsanity.com/paper/2306.05424/) (2023)
- [Dense-Captioning Events in Videos](https://mlsanity.com/paper/1705.00754/) (2017)
- [LISA: Reasoning Segmentation via Large Language Model](https://mlsanity.com/paper/2308.00692/) (2023)
- [LongVideoBench: A Benchmark for Long-context Interleaved Video-Language Understanding](https://mlsanity.com/paper/2407.15754/) (2024)
- [Localizing Moments in Video with Natural Language](https://mlsanity.com/paper/1708.01641/) (2017)
- [Towards Automatic Learning of Procedures from Web Instructional Videos](https://mlsanity.com/paper/1703.09788/) (2017)
- [LLaVA-Video: Video Instruction Tuning With Synthetic Data](https://mlsanity.com/paper/2410.02713/) (2024)
- [Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding](https://mlsanity.com/paper/1604.01753/) (2016)
- [VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding](https://mlsanity.com/paper/2501.13106/) (2025)
- [TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding](https://mlsanity.com/paper/2312.02051/) (2023)
- [Seed1.5-VL Technical Report](https://mlsanity.com/paper/2505.07062/) (2025)
- [A Survey on Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2402.00253/) (2024)
- [LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding](https://mlsanity.com/paper/2410.17434/) (2024)
- [COIN: A Large-scale Dataset for Comprehensive Instructional Video Analysis](https://mlsanity.com/paper/1903.02874/) (2019)
- [Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language](https://mlsanity.com/paper/1912.03590/) (2019)
- [VTimeLLM: Empower LLM to Grasp Video Moments](https://mlsanity.com/paper/2311.18445/) (2023)
- [Span-based Localizing Network for Natural Language Video Localization](https://mlsanity.com/paper/2004.13931/) (2020)
- [VideoChat-Flash: Hierarchical Compression for Long-Context Video Modeling](https://mlsanity.com/paper/2501.00574/) (2024)
- [UniVTG: Towards Unified Video-Language Temporal Grounding](https://mlsanity.com/paper/2307.16715/) (2023)
- [Adaptive Keyframe Sampling for Long Video Understanding](https://mlsanity.com/paper/2502.21271/) (2025)
- [Query-Dependent Video Representation for Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2303.13874/) (2023)

## Built on by (most cited first, 27 of 27)

- [TimeLens: Rethinking Video Temporal Grounding with Multimodal LLMs](https://mlsanity.com/paper/2512.14698/) (2025)
- [Open-o3-Video: Grounded Video Reasoning with Explicit Spatio-Temporal Evidence](https://mlsanity.com/paper/2510.20579/) (2025)
- [Video-OPD: Efficient Post-Training of Multimodal Large Language Models for Temporal Video Grounding via On-Policy Distillation](https://mlsanity.com/paper/2602.02994/) (2026)
- [Zoom-Zero: Reinforced Coarse-to-Fine Video Understanding via Temporal Zoom-in](https://mlsanity.com/paper/2512.14273/) (2025)
- [TimeScope: Towards Task-Oriented Temporal Grounding In Long Videos](https://mlsanity.com/paper/2509.26360/) (2025)
- [EgoExo-Con: Exploring View-Invariant Video Temporal Understanding](https://mlsanity.com/paper/2510.26113/) (2025)
- [VideoLoom: A Video Large Language Model for Joint Spatial-Temporal Understanding](https://mlsanity.com/paper/2601.07290/) (2026)
- [Think with Grounding: Curriculum Reinforced Reasoning with Video Grounding for Long Video Understanding](https://mlsanity.com/paper/2602.18702/) (2026)
- [OmniVTG: A Large-Scale Dataset and Training Paradigm for Open-World Video Temporal Grounding](https://mlsanity.com/paper/2604.25276/) (2026)
- [Towards One-to-Many Temporal Grounding](https://mlsanity.com/paper/2606.06294/) (2026)
- [Temporal-Aware Reasoning Optimization for Video Temporal Grounding](https://mlsanity.com/paper/2606.09248/) (2026)
- [Image-to-Video Transfer Learning based on Image-Language Foundation Models: A Comprehensive Survey](https://mlsanity.com/paper/2510.10671/) (2025)
- [Weaver: End-to-End Agentic System Training for Video Interleaved Reasoning](https://mlsanity.com/paper/2602.05829/) (2026)
- [VersaViT: Enhancing MLLM Vision Backbones via Task-Guided Optimization](https://mlsanity.com/paper/2602.09934/) (2026)
- [SlotVTG: Object-Centric Adapter for Generalizable Video Temporal Grounding](https://mlsanity.com/paper/2603.25733/) (2026)
- [STORM: Internalized Modeling for Spatial-Temporal Reasoning in Video-Language Models](https://mlsanity.com/paper/2605.26014/) (2026)
- [EVIDENT: Routing MLLM Adaptation through Entity-Grounded Visual Evidence for Cross-Domain Video Temporal Grounding](https://mlsanity.com/paper/2605.26104/) (2026)
- [Beyond Single-Sample: Reliable Multi-Sample Distillation for Video Understanding](https://mlsanity.com/paper/2603.11423/) (2026)
- [UniversalVTG: A Universal and Lightweight Foundation Model for Video Temporal Grounding](https://mlsanity.com/paper/2604.08522/) (2026)
- [Interactive Episodic Memory with User Feedback](https://mlsanity.com/paper/2604.24893/) (2026)
- [OSGNet with MLLM Reranking @ Ego4D Episodic Memory Challenge 2026](https://mlsanity.com/paper/2605.20818/) (2026)
- [TimePLE: Rethinking Temporal Representation for Video Temporal Grounding](https://mlsanity.com/paper/2607.23951/) (2026)
- [Your VLM Already Knows When: Training-Free Temporal Grounding by Asking Yes or No](https://mlsanity.com/paper/2608.08315/) (2026)
- [Locate Anything in Videos: Rethinking Efficient Generative Spatio-Temporal Video Grounding](https://mlsanity.com/paper/2608.28192/) (2026)
- [Multi-Agent Self-Improving Reinforcement Learning for Video Reasoning](https://mlsanity.com/paper/2608.28675/) (2026)
- [IAE-VTG: Interaction-Aligned Action-Entity Video Temporal Grounding](https://mlsanity.com/paper/2609.09736/) (2026)
- [Open-o3-Video-v2: Seek, Ground, and Reason with Verifiable Spatio-Temporal Evidence](https://mlsanity.com/paper/2610.17656/) (2026)

## Abstract (arXiv)

This paper presents a computational model for universal video temporal grounding, which accurately localizes temporal moments in videos based on natural language queries (e.g., questions or descriptions). Unlike existing methods that are often limited to specific video domains or durations, we propose UniTime, a robust and universal video grounding model leveraging the strong vision-language understanding capabilities of generative Multi-modal Large Language Models (MLLMs). Our model effectively handles videos of diverse views, genres, and lengths while comprehending complex language queries. The key contributions include: (i) We consider steering strong MLLMs for temporal grounding in videos. To enable precise timestamp outputs, we incorporate temporal information by interleaving timestamp tokens with video tokens. (ii) By training the model to handle videos with different input granularities through adaptive frame scaling, our approach achieves robust temporal grounding for both short and long videos. (iii) Comprehensive experiments show that UniTime outperforms state-of-the-art approaches in both zero-shot and dataset-specific finetuned settings across five public temporal grounding benchmarks. (iv) When employed as a preliminary moment retriever for long-form video question-answering (VideoQA), UniTime significantly improves VideoQA accuracy, highlighting its value for complex video understanding tasks.