mlsanityData to 7 Oct 2026

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

arXiv 2405.13382 ↗First posted 22 May 2024cs.CVcited by 85

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Cited by 20 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

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.

Key facts. Cited by 85 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 May 2024.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 9; 2025: 39; 2026: 37.

85 papers.

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  1. 2025Time-R1: Post-Training Large Vision Language Model for Temporal Video Groundingcited by 138
  2. 2024TRACE: Temporal Grounding Video LLM via Causal Event Modelingcited by 87
  3. 2025Tarsier2: Advancing Large Vision-Language Models from Detailed Video Description to Comprehensive Video Understandingcited by 80
  4. 2024E.T. Bench: Towards Open-Ended Event-Level Video-Language Understandingcited by 68
  5. 2024Number it: Temporal Grounding Videos like Flipping Mangacited by 65
  6. 2025LLaVA-ST: A Multimodal Large Language Model for Fine-Grained Spatial-Temporal Understandingcited by 57
  7. 2025DeepVideo-R1: Video Reinforcement Fine-Tuning via Difficulty-aware Regressive GRPOcited by 57
  8. 2025Video-XL-Pro: Reconstructive Token Compression for Extremely Long Video Understandingcited by 49
  9. 2024VideoLLM Knows When to Speak: Enhancing Time-Sensitive Video Comprehension with Video-Text Duet Interaction Formatcited by 44
  10. 2026LLaVA-OneVision-2: Towards Next-Generation Perceptual Intelligencecited by 31
  11. 2025SpaceVLLM: Endowing Multimodal Large Language Model with Spatio-Temporal Video Grounding Capabilitycited by 28
  12. 2025Universal Video Temporal Grounding with Generative Multi-modal Large Language Modelscited by 26
38 more
  1. 2025Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiencycited by 25
  2. 2025LiveStar: Live Streaming Assistant for Real-World Online Video Understandingcited by 22
  3. 2025A Survey on Video Temporal Grounding with Multimodal Large Language Modelcited by 19
  4. 2024On the Consistency of Video Large Language Models in Temporal Comprehensioncited by 18
  5. 2025Datasets and Recipes for Video Temporal Grounding via Reinforcement Learningcited by 18
  6. 2024TimeRefine: Temporal Grounding with Time Refining Video LLMcited by 15
  7. 2025TempSamp-R1: Effective Temporal Sampling with Reinforcement Fine-Tuning for Video LLMscited by 15
  8. 2025Xiaomi MiMo-VL-Miloco Technical Reportcited by 15
  9. 2025TimeExpert: An Expert-Guided Video LLM for Video Temporal Groundingcited by 14
  10. 2025Shot-by-Shot: Film-Grammar-Aware Training-Free Audio Description Generationcited by 13
  11. 2024Seq2Time: Sequential Knowledge Transfer for Video LLM Temporal Groundingcited by 12
  12. 2025iMOVE: Instance-Motion-Aware Video Understandingcited by 10
  13. 2025ZoomV: Temporal Zoom-in for Efficient Long Video Understandingcited by 10
  14. 2025VideoExpert: Augmented LLM for Temporal-Sensitive Video Understandingcited by 8
  15. 2025DisTime: Distribution-based Time Representation for Video Large Language Modelscited by 7
  16. 2025AViLA: Asynchronous Vision-Language Agent for Streaming Multimodal Data Interactioncited by 6
  17. 2025Strefer: Empowering Video LLMs with Space-Time Referring and Reasoning via Synthetic Instruction Datacited by 6
  18. 2026AdaCodec: A Predictive Visual Code for Video MLLMscited by 6
  19. 2026VideoChat3: Fully Open Video MLLM for Efficient and Generalist Video Understandingcited by 6
  20. 2025SafePLUG: Empowering Multimodal LLMs with Pixel-Level Insight and Temporal Grounding for Traffic Accident Understandingcited by 5
  21. 2025TimeScope: Towards Task-Oriented Temporal Grounding In Long Videoscited by 5
  22. 2025ChronusOmni: Improving Time Awareness of Omni Large Language Modelscited by 5
  23. 2026TagSpeech: End-to-End Multi-Speaker ASR and Diarization with Fine-Grained Temporal Groundingcited by 5
  24. 2026VideoLoom: A Video Large Language Model for Joint Spatial-Temporal Understandingcited by 5
  25. 2025MomentSeg: Moment-Centric Sampling for Enhanced Video Pixel Understandingcited by 4
  26. 2025EgoExo-Con: Exploring View-Invariant Video Temporal Understandingcited by 4
  27. 2025Factorized Learning for Temporally Grounded Video-Language Modelscited by 4
  28. 2026ArrowGEV: Grounding Events in Video via Learning the Arrow of Timecited by 4
  29. 2025HierSum: A Global and Local Attention Mechanism for Video Summarizationcited by 3
  30. 2026EvoGround: Self-Evolving Video Agents for Video Temporal Groundingcited by 3
  31. 2024ActPrompt: In-Domain Feature Adaptation via Action Cues for Video Temporal Groundingcited by 2
  32. 2025Measure Twice, Cut Once: A Semantic-Oriented Approach to Video Temporal Localization with Video LLMscited by 2
  33. 2025Empowering Multimodal LLMs with External Tools: A Comprehensive Surveycited by 2
  34. 2025AHA -- Predicting What Matters Next: Online Highlight Detection Without Looking Aheadcited by 2
  35. 2025Image-to-Video Transfer Learning based on Image-Language Foundation Models: A Comprehensive Surveycited by 2
  36. 2025Learning to Refuse: Refusal-Aware Reinforcement Fine-Tuning for Hard-Irrelevant Queries in Video Temporal Groundingcited by 2
  37. 2025UFVideo: Towards Unified Fine-Grained Video Cooperative Understanding with Large Language Modelscited by 2
  38. 2026STRIDE: When to Speak Meets Sequence Denoising for Streaming Video Understandingcited by 2

50 most cited of 85 All 85 (JSON)

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  1. 2025TimeLens: Rethinking Video Temporal Grounding with Multimodal LLMs
  2. 2025Enrich and Detect: Video Temporal Grounding with Multimodal LLMs
  3. 2024Training-free Video Temporal Grounding using Large-scale Pre-trained Models
  4. 2025Harnessing Object Grounding for Time-Sensitive Video Understanding
  5. 2026Towards One-to-Many Temporal Grounding
  6. 2025TAG: A Simple Yet Effective Temporal-Aware Approach for Zero-Shot Video Temporal Grounding
  7. 2025VideoITG: Multimodal Video Understanding with Instructed Temporal Grounding
  8. 2024Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models