# Enrich and Detect: Video Temporal Grounding with Multimodal LLMs

arXiv: https://arxiv.org/abs/2510.17023 · HTML: https://mlsanity.com/paper/2510.17023/

## Key facts

- Cited by 12 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 112 of its 133 references that are in the map.
- First posted on arXiv 19 Oct 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 112)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding](https://mlsanity.com/paper/2306.02858/) (2023)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval](https://mlsanity.com/paper/2104.00650/) (2021)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [MVBench: A Comprehensive Multi-modal Video Understanding Benchmark](https://mlsanity.com/paper/2311.17005/) (2023)
- [Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models](https://mlsanity.com/paper/2306.05424/) (2023)
- [HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips](https://mlsanity.com/paper/1906.03327/) (2019)
- [VideoChat: Chat-Centric Video Understanding](https://mlsanity.com/paper/2305.06355/) (2023)
- [EVA-CLIP: Improved Training Techniques for CLIP at Scale](https://mlsanity.com/paper/2303.15389/) (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)
- [NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions](https://mlsanity.com/paper/2105.08276/) (2021)
- [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)
- [TALL: Temporal Activity Localization via Language Query](https://mlsanity.com/paper/1705.02101/) (2017)
- [VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks](https://mlsanity.com/paper/2305.11175/) (2023)
- [GLaMM: Pixel Grounding Large Multimodal Model](https://mlsanity.com/paper/2311.03356/) (2023)
- [TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding](https://mlsanity.com/paper/2312.02051/) (2023)

## Built on by (most cited first, 12 of 12)

- [EvoGround: Self-Evolving Video Agents for Video Temporal Grounding](https://mlsanity.com/paper/2605.13803/) (2026)
- [T2SGrid: Temporal-to-Spatial Gridification for Video Temporal Grounding](https://mlsanity.com/paper/2603.06973/) (2026)
- [DRIFT: A Residual Flow Adapter for Decoding Continuous Outputs in Vision-Language Models](https://mlsanity.com/paper/2606.05758/) (2026)
- [UniversalVTG: A Universal and Lightweight Foundation Model for Video Temporal Grounding](https://mlsanity.com/paper/2604.08522/) (2026)
- [Retrieving Any Relevant Moments: Benchmark and Models for Generalized Moment Retrieval](https://mlsanity.com/paper/2605.02623/) (2026)
- [Efficient Spatio-Temporal Grounding with Multimodal Large Models via Second-Level Tracking and RL Verification](https://mlsanity.com/paper/2606.29023/) (2026)
- [AnyGroundBench: A Specialized-Domain Benchmark for Video Grounding in Vision-Language Models](https://mlsanity.com/paper/2607.02269/) (2026)
- [D'ej`a Cue: Localizing States in Object Histories via Vocabulary-Relative Coordinates](https://mlsanity.com/paper/2608.02044/) (2026)
- [Your VLM Already Knows When: Training-Free Temporal Grounding by Asking Yes or No](https://mlsanity.com/paper/2608.08315/) (2026)
- [FADE: From Passive Verification to Active Discovery in Counterfactual Video Understanding](https://mlsanity.com/paper/2608.10764/) (2026)
- [ViTAL-X: Video-Text Alignment with Cross-Modal Temporal Edits](https://mlsanity.com/paper/2609.00505/) (2026)
- [Grounding with Confidence: Controllable Generative Video Temporal Grounding](https://mlsanity.com/paper/2609.39883/) (2026)

## Abstract (arXiv)

We introduce ED-VTG, a method for fine-grained video temporal grounding utilizing multi-modal large language models. Our approach harnesses the capabilities of multimodal LLMs to jointly process text and video, in order to effectively localize natural language queries in videos through a two-stage process. Rather than being directly grounded, language queries are initially transformed into enriched sentences that incorporate missing details and cues to aid in grounding. In the second stage, these enriched queries are grounded, using a lightweight decoder, which specializes at predicting accurate boundaries conditioned on contextualized representations of the enriched queries. To mitigate noise and reduce the impact of hallucinations, our model is trained with a multiple-instance-learning objective that dynamically selects the optimal version of the query for each training sample. We demonstrate state-of-the-art results across various benchmarks in temporal video grounding and paragraph grounding settings. Experiments reveal that our method significantly outperforms all previously proposed LLM-based temporal grounding approaches and is either superior or comparable to specialized models, while maintaining a clear advantage against them in zero-shot evaluation scenarios.