# VTG-LLM: Integrating Timestamp Knowledge into Video LLMs for Enhanced Video Temporal Grounding

arXiv: https://arxiv.org/abs/2405.13382 · HTML: https://mlsanity.com/paper/2405.13382/

## Key facts

- Cited by 85 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2024.

## Built on by (most cited first, 30 of 85)

- [Time-R1: Post-Training Large Vision Language Model for Temporal Video Grounding](https://mlsanity.com/paper/2503.13377/) (2025)
- [TRACE: Temporal Grounding Video LLM via Causal Event Modeling](https://mlsanity.com/paper/2410.05643/) (2024)
- [Tarsier2: Advancing Large Vision-Language Models from Detailed Video Description to Comprehensive Video Understanding](https://mlsanity.com/paper/2501.07888/) (2025)
- [E.T. Bench: Towards Open-Ended Event-Level Video-Language Understanding](https://mlsanity.com/paper/2409.18111/) (2024)
- [Number it: Temporal Grounding Videos like Flipping Manga](https://mlsanity.com/paper/2411.10332/) (2024)
- [DeepVideo-R1: Video Reinforcement Fine-Tuning via Difficulty-aware Regressive GRPO](https://mlsanity.com/paper/2506.07464/) (2025)
- [LLaVA-ST: A Multimodal Large Language Model for Fine-Grained Spatial-Temporal Understanding](https://mlsanity.com/paper/2501.08282/) (2025)
- [Video-XL-Pro: Reconstructive Token Compression for Extremely Long Video Understanding](https://mlsanity.com/paper/2503.18478/) (2025)
- [VideoLLM Knows When to Speak: Enhancing Time-Sensitive Video Comprehension with Video-Text Duet Interaction Format](https://mlsanity.com/paper/2411.17991/) (2024)
- [LLaVA-OneVision-2: Towards Next-Generation Perceptual Intelligence](https://mlsanity.com/paper/2605.25979/) (2026)
- [SpaceVLLM: Endowing Multimodal Large Language Model with Spatio-Temporal Video Grounding Capability](https://mlsanity.com/paper/2503.13983/) (2025)
- [Universal Video Temporal Grounding with Generative Multi-modal Large Language Models](https://mlsanity.com/paper/2506.18883/) (2025)
- [Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency](https://mlsanity.com/paper/2506.01908/) (2025)
- [LiveStar: Live Streaming Assistant for Real-World Online Video Understanding](https://mlsanity.com/paper/2511.05299/) (2025)
- [A Survey on Video Temporal Grounding with Multimodal Large Language Model](https://mlsanity.com/paper/2508.10922/) (2025)
- [Datasets and Recipes for Video Temporal Grounding via Reinforcement Learning](https://mlsanity.com/paper/2507.18100/) (2025)
- [On the Consistency of Video Large Language Models in Temporal Comprehension](https://mlsanity.com/paper/2411.12951/) (2024)
- [TimeRefine: Temporal Grounding with Time Refining Video LLM](https://mlsanity.com/paper/2412.09601/) (2024)
- [TempSamp-R1: Effective Temporal Sampling with Reinforcement Fine-Tuning for Video LLMs](https://mlsanity.com/paper/2509.18056/) (2025)
- [Xiaomi MiMo-VL-Miloco Technical Report](https://mlsanity.com/paper/2512.17436/) (2025)
- [TimeExpert: An Expert-Guided Video LLM for Video Temporal Grounding](https://mlsanity.com/paper/2508.01699/) (2025)
- [Shot-by-Shot: Film-Grammar-Aware Training-Free Audio Description Generation](https://mlsanity.com/paper/2504.01020/) (2025)
- [Seq2Time: Sequential Knowledge Transfer for Video LLM Temporal Grounding](https://mlsanity.com/paper/2411.16932/) (2024)
- [iMOVE: Instance-Motion-Aware Video Understanding](https://mlsanity.com/paper/2502.11594/) (2025)
- [ZoomV: Temporal Zoom-in for Efficient Long Video Understanding](https://mlsanity.com/paper/2504.01407/) (2025)
- [VideoChat3: Fully Open Video MLLM for Efficient and Generalist Video Understanding](https://mlsanity.com/paper/2607.14935/) (2026)
- [VideoExpert: Augmented LLM for Temporal-Sensitive Video Understanding](https://mlsanity.com/paper/2504.07519/) (2025)
- [DisTime: Distribution-based Time Representation for Video Large Language Models](https://mlsanity.com/paper/2505.24329/) (2025)
- [AdaCodec: A Predictive Visual Code for Video MLLMs](https://mlsanity.com/paper/2606.02569/) (2026)
- [AViLA: Asynchronous Vision-Language Agent for Streaming Multimodal Data Interaction](https://mlsanity.com/paper/2506.18472/) (2025)

## Abstract (arXiv)

Video Temporal Grounding (VTG) strives to accurately pinpoint event timestamps in a specific video using linguistic queries, significantly impacting downstream tasks like video browsing and editing. Unlike traditional task-specific models, Video Large Language Models (video LLMs) can handle multiple tasks concurrently in a zero-shot manner. Consequently, exploring the application of video LLMs for VTG tasks has become a burgeoning research area. However, despite considerable advancements in video content understanding, video LLMs often struggle to accurately pinpoint timestamps within videos, limiting their effectiveness in VTG tasks. To address this, we introduce VTG-LLM, a model designed to enhance video LLMs' timestamp localization abilities. Our approach includes: (1) effectively integrating timestamp knowledge into visual tokens; (2) incorporating absolute-time tokens to manage timestamp knowledge without concept shifts; and (3) introducing a lightweight, high-performance, slot-based token compression technique designed to accommodate the demands of a large number of frames to be sampled for VTG tasks. Additionally, we present VTG-IT-120K, a collection of publicly available VTG datasets that we have re-annotated to improve upon low-quality annotations. Our comprehensive experiments demonstrate the superior performance of VTG-LLM in comparison to other video LLM methods across a variety of VTG tasks.