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Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models
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Video Large Language Models (Video-LLMs) have demonstrated remarkable capabilities in coarse-grained video understanding, however, they struggle with fine-grained temporal grounding. In this paper, we introduce Grounded-VideoLLM, a novel Video-LLM adept at perceiving and reasoning over specific video moments in a fine-grained manner. We identify that current Video-LLMs have limitations for fine-grained video understanding since they lack effective temporal modeling and timestamp representation. In light of this, we sharpen our model by incorporating (1) an additional temporal stream to encode the relationships between frames and (2) discrete temporal tokens enriched with specific time knowledge to represent timestamps. To optimize the training of Grounded-VideoLLM, we employ a multi-stage training scheme, beginning with simple video-captioning tasks and progressively introducing video temporal grounding tasks of increasing complexity. To further enhance Grounded-VideoLLM's temporal reasoning capability, we also curate a grounded VideoQA dataset by an automatic annotation pipeline. Extensive experiments demonstrate that Grounded-VideoLLM not only excels in fine-grained grounding tasks such as temporal sentence grounding, dense video captioning, and grounded VideoQA, but also shows great potential as a versatile video assistant for general video understanding.
Forward citations
Cited by 18 Pith papers
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TimeLens2: Generalist Video Temporal Grounding with Multimodal LLMs
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An open 4B video MLLM with inflated-3D ViT tokenization and adaptive streaming perception outperforms comparable open models on general, long-video, and streaming benchmarks while using fewer visual tokens.
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TimeThink adds step-wise temporal process rewards (max IoU of referenced intervals) to GRPO for Video-LLMs, improving grounding and reasoning over outcome-only RL baselines.
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Strefer: Empowering Video LLMs with Space-Time Referring and Reasoning via Synthetic Instruction Data
Adding Strefer's synthetic space-time reference questions to video instruction tuning improves mask-referred description/QA, timestamp QA, and temporal reasoning over a video-LLM baseline.
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VLM4D: Towards Spatiotemporal Awareness in Vision Language Models
VLM4D benchmarks spatiotemporal reasoning in VLMs and finds large gaps versus humans, with proposed methods showing partial improvement.
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"Harmless to You, Hurtful to Me!": Investigating the Detection of Toxic Languages Grounded in the Perspective of Youth
The authors construct the first Chinese youth-toxicity dataset, show that youth and adult perceptions of toxic language diverge, and report that adding contextual meta information improves detection accuracy.
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IPFormer-VideoLLM: Enhancing Multi-modal Video Understanding for Multi-shot Scenes
A new multi-shot video dataset and an instance-prompt video LLM report large gains, but the main benchmark is built by the same authors and the model is not released.
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Scene-R1: Video-Grounded Large Language Models for 3D Scene Reasoning without 3D Annotations
Video-grounded RL with two-stage 2D grounding and SAM2 lifting enables 3D object localization and QA without dense 3D instance supervision.
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Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency
A GRPO-based post-training recipe for video LLMs using discrete QA rewards plus continuous temporal IoU rewards with variance-based data selection outperforms SFT and Video-R1.
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TimeExpert: An Expert-Guided Video LLM for Video Temporal Grounding
TimeExpert applies dynamic mixture-of-experts routing to video temporal grounding, reporting small state-of-the-art gains over TRACE on dense video captioning, moment retrieval, and highlight detection.
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Datasets and Recipes for Video Temporal Grounding via Reinforcement Learning
A supervised fine-tuning plus difficulty-filtered reinforcement learning recipe improves video temporal grounding on three benchmarks, with datasets and models released.
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When and What: Diffusion-Grounded VideoLLM with Entity Aware Segmentation for Long Video Understanding
A video-LLM that uses diffusion features, segmentation-guided object tokens, and discrete time tokens to improve temporal grounding, but its claimed SOTA results are not supported by its own tables.
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A Survey on Video Temporal Grounding with Multimodal Large Language Model
A taxonomized review of video temporal grounding with multimodal large language models, covering model roles, training paradigms, feature processing, benchmarks, and open problems.
- MUPA: Towards Multi-Path Agentic Reasoning for Grounded Video Question Answering
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