GroundVTS: Visual Token Sampling in Multimodal Large Language Models for Video Temporal Grounding

• URL: http://arxiv.org/abs/2604.02093v1
• Date: Thu, 02 Apr 2026 14:19:58 GMT
• Title: GroundVTS: Visual Token Sampling in Multimodal Large Language Models for Video Temporal Grounding
• Abstract summary: Video temporal grounding (VTG) is a critical task in video and understanding a key capability for extending large language models (Vid-LLMs) to broader applications.<n>Existing Vid-LLMs rely on uniform frame sampling to extract video information, resulting in a sparse distribution of key frames and the loss of crucial temporal cues.<n>We propose Grounded Visual Token Sampling (GroundVTS), a Vid-LLM architecture that focuses on the most informative temporal segments.
• Score: 8.196520511153368
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video temporal grounding (VTG) is a critical task in video understanding and a key capability for extending video large language models (Vid-LLMs) to broader applications. However, existing Vid-LLMs rely on uniform frame sampling to extract video information, resulting in a sparse distribution of key frames and the loss of crucial temporal cues. To address this limitation, we propose Grounded Visual Token Sampling (GroundVTS), a Vid-LLM architecture that focuses on the most informative temporal segments. GroundVTS employs a fine-grained, query-guided mechanism to filter visual tokens before feeding them into the LLM, thereby preserving essential spatio-temporal information and maintaining temporal coherence. Futhermore, we introduce a progressive optimization strategy that enables the LLM to effectively adapt to the non-uniform distribution of visual features, enhancing its ability to model temporal dependencies and achieve precise video localization. We comprehensively evaluate GroundVTS on three standard VTG benchmarks, where it outperforms existing methods, achieving a 7.7-point improvement in mIoU for moment retrieval and 12.0-point improvement in mAP for highlight detection. Code is available at https://github.com/Florence365/GroundVTS.

Related papers

• SlowFocus: Enhancing Fine-grained Temporal Understanding in Video LLM [36.28285195488772]
  Large language models (LLMs) have demonstrated exceptional capabilities in text understanding.<n>Vid-LLMs struggle to simultaneously retain high-quality frame-level semantic information.<n>This limitation hinders the advancement of Vid-LLMs towards fine-grained video understanding.
  arXiv  Detail & Related papers  (2026-02-03T14:39:16Z)
• TimeLens: Rethinking Video Temporal Grounding with Multimodal LLMs [81.78017865436816]
  We present TimeLens, a systematic investigation into building MLLMs with strong video temporal grounding ability.<n>We first expose critical quality issues in existing VTG benchmarks and introduce TimeLens-Bench, comprising meticulously re-annotated versions of three popular benchmarks.<n>We also address noisy training data through an automated re-annotation pipeline, yielding TimeLens-100K, a large-scale, high-quality training dataset.
  arXiv  Detail & Related papers  (2025-12-16T18:59:58Z)
• Harnessing Synthetic Preference Data for Enhancing Temporal Understanding of Video-LLMs [54.502280390499756]
  We propose TimeWarp to create a targeted synthetic temporal dataset to fine-tune the model's responses to encourage it to focus on the given input video.<n>We demonstrate that when our method is applied to existing models, it significantly improves performance on temporal understanding benchmarks.
  arXiv  Detail & Related papers  (2025-10-04T21:48:40Z)
• VT-LVLM-AR: A Video-Temporal Large Vision-Language Model Adapter for Fine-Grained Action Recognition in Long-Term Videos [8.711160469571942]
  This paper introduces VT-LVLM-AR (Video Large Vision-Language Model Adapter for Action Recognition), a novel framework designed to bridge this gap.<n>VTEM transforms raw video into semantically rich, and temporally coherent "visual event sequences"<n>The framework consistently achieves state-of-the-art performance, surpassing existing methods.
  arXiv  Detail & Related papers  (2025-08-21T18:03:16Z)
• Tempo-R0: A Video-MLLM for Temporal Video Grounding through Efficient Temporal Sensing Reinforcement Learning [6.9627404612894335]
  Temporal Video Grounding (TVG) requires pinpointing relevant temporal segments from video based on language query.<n>We propose Tempo-R0: a Video Multimodal Large Language Model (Video-MLLM) for the temporal video grounding task.<n>Our method accomplishes a notable advantage over SOTA solutions by around 3.5% on the original QVHighlights testbench.
  arXiv  Detail & Related papers  (2025-07-07T06:51:40Z)
• Universal Video Temporal Grounding with Generative Multi-modal Large Language Models [59.781211641591405]
  This paper presents a computational model for universal video temporal grounding, which accurately localizes temporal moments in videos based on natural language queries.<n>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)<n>Our model effectively handles videos of diverse views, genres, and lengths while comprehending complex language queries.
  arXiv  Detail & Related papers  (2025-06-23T17:53:18Z)
• Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency [56.475612147721264]
  We propose a dual-reward formulation that supervises both semantic and temporal reasoning through discrete and continuous reward signals.<n>We evaluate our approach across eight representative video understanding tasks, including VideoQA, Temporal Video Grounding, and Grounded VideoQA.<n>Results underscore the importance of reward design and data selection in advancing reasoning-centric video understanding with MLLMs.
  arXiv  Detail & Related papers  (2025-06-02T17:28:26Z)
• STORM: Token-Efficient Long Video Understanding for Multimodal LLMs [116.4479155699528]
  STORM is a novel architecture incorporating a dedicated temporal encoder between the image encoder and the Video-LLMs.<n>We show that STORM achieves state-of-the-art results across various long video understanding benchmarks.
  arXiv  Detail & Related papers  (2025-03-06T06:17:38Z)
• Temporal Preference Optimization for Long-Form Video Understanding [63.196246578583136]
  Temporal Preference Optimization (TPO) is a novel post-training framework designed to enhance the temporal grounding capabilities of video-LMMs.<n>TPO significantly enhances temporal understanding while reducing reliance on manually annotated data.<n>LLaVA-Video-TPO establishes itself as the leading 7B model on the Video-MME benchmark.
  arXiv  Detail & Related papers  (2025-01-23T18:58:03Z)
• FlashVTG: Feature Layering and Adaptive Score Handling Network for Video Temporal Grounding [25.21011724370177]
  Text-guided Video Temporal Grounding (VTG) aims to localize relevant segments in untrimmed videos based on textual descriptions.<n>We introduce FlashVTG, a framework featuring a Temporal Feature Layering (TFL) module and an Adaptive Score Refinement (ASR) module.<n>FlashVTG achieves state-of-the-art performance on four widely adopted datasets in both Moment Retrieval (MR) and Highlight Detection (HD)
  arXiv  Detail & Related papers  (2024-12-18T02:23:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.