Time-R1: Post-Training Large Vision Language Model for Temporal Video Grounding

• URL: http://arxiv.org/abs/2503.13377v3
• Date: Sun, 29 Jun 2025 08:11:35 GMT
• Title: Time-R1: Post-Training Large Vision Language Model for Temporal Video Grounding
• Authors: Ye Wang, Ziheng Wang, Boshen Xu, Yang Du, Kejun Lin, Zihan Xiao, Zihao Yue, Jianzhong Ju, Liang Zhang, Dingyi Yang, Xiangnan Fang, Zewen He, Zhenbo Luo, Wenxuan Wang, Junqi Lin, Jian Luan, Qin Jin,
• Abstract summary: Temporal Video Grounding (TVG) is a core challenge in long-form video understanding.<n>Recent Large Vision-Language Models (LVLMs) have shown early promise in tackling TVG through supervised fine-tuning.<n>We propose a novel post-training framework that enhances the generalization capabilities of LVLMs via reinforcement learning.
• Score: 57.26400319795876
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Temporal Video Grounding (TVG), the task of locating specific video segments based on language queries, is a core challenge in long-form video understanding. While recent Large Vision-Language Models (LVLMs) have shown early promise in tackling TVG through supervised fine-tuning (SFT), their abilities to generalize remain limited. To address this, we propose a novel post-training framework that enhances the generalization capabilities of LVLMs via reinforcement learning (RL). Specifically, our contributions span three key directions: (1) Time-R1: we introduce a reasoning-guided post-training framework via RL with verifiable reward to enhance the capabilities of LVLMs on the TVG task. (2) TimeRFT: we explore data-efficient post-training strategies on our curated RL-friendly dataset, which trains the model to progressively comprehend difficult samples, leading to better generalization. (3) TVGBench: we carefully construct a small yet comprehensive benchmark for LVLM evaluation, assessing 11 types of queries and featuring balanced distributions across both videos and queries. Extensive experiments demonstrate that Time-R1 achieves state-of-the-art performance across multiple downstream datasets using only 2.5K training data, while improving its general video understanding capabilities.

Related papers

• Datasets and Recipes for Video Temporal Grounding via Reinforcement Learning [9.8322406322074]
  Video Temporal Grounding aims to localize relevant temporal segments in videos given natural language queries.<n>Existing approaches often suffer from limited temporal awareness and poor generalization.<n>We introduce a two-stage training framework that integrates supervised fine-tuning with reinforcement learning.
  arXiv  Detail & Related papers  (2025-07-24T05:24:01Z)
• VLN-R1: Vision-Language Navigation via Reinforcement Fine-Tuning [77.34267241692706]
  Vision-Language Navigation (VLN) is a core challenge in embodied AI, requiring agents to navigate real-world environments using natural language instructions.<n>We propose VLN-R1, an end-to-end framework that leverages Large Vision-Language Models (LVLM) to directly translate egocentric video streams into continuous navigation actions.
  arXiv  Detail & Related papers  (2025-06-20T17:59:59Z)
• Prompts to Summaries: Zero-Shot Language-Guided Video Summarization [12.200609701777907]
  We introduce Prompts-to-Summaries: the first zero-shot, text-queryable video summarizer.<n>It converts off-the-shelf video-language models (VidLMs) captions into user-guided skims via large language models (LLMs) judging.<n>Our pipeline generates rich scene-level descriptions through a memory-efficient, batch-style VidLM prompting scheme.<n>On SumMe and TVSum, our data-free approach surpasses all prior data-hungry unsupervised methods.
  arXiv  Detail & Related papers  (2025-06-12T15:23:11Z)
• How Important are Videos for Training Video LLMs? [55.965474658745315]
  We present findings indicating Video LLMs are more capable of temporal reasoning after image-only training than one would assume.<n>We introduce a simple finetuning scheme involving sequences of annotated images and questions targeting temporal capabilities.<n>This suggests suboptimal utilization of rich temporal features found in real video by current models.
  arXiv  Detail & Related papers  (2025-06-07T21:32:19Z)
• 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)
• ViaRL: Adaptive Temporal Grounding via Visual Iterated Amplification Reinforcement Learning [68.76048244253582]
  We introduce ViaRL, the first framework to leverage rule-based reinforcement learning (RL) for optimizing frame selection in video understanding.<n>ViaRL utilizes the answer accuracy of a downstream model as a reward signal to train a frame selector through trial-and-error.<n>ViaRL consistently delivers superior temporal grounding performance and robust generalization across diverse video understanding tasks.
  arXiv  Detail & Related papers  (2025-05-21T12:29:40Z)
• Exploring the Effect of Reinforcement Learning on Video Understanding: Insights from SEED-Bench-R1 [53.894789613838654]
  We introduce SEED-Bench-R1, a benchmark designed to evaluate post-training methods for MLLMs in video understanding.<n>It includes intricate real-world videos and complex everyday planning tasks in the format of multiple-choice questions.<n>Using Qwen2-VL-Instruct-7B as a base model, we compare RL with supervised fine-tuning (SFT)<n>Our detailed analysis reveals that RL enhances visual perception but often produces less coherent reasoning chains.
  arXiv  Detail & Related papers  (2025-03-31T17:55:23Z)
• TimeLoc: A Unified End-to-End Framework for Precise Timestamp Localization in Long Videos [50.04992164981131]
  Temporal localization in untrimmed videos is crucial for video understanding but remains challenging.<n>This task encompasses several subtasks, including temporal action localization, temporal video grounding, moment retrieval, and generic event boundary detection.<n>We propose TimeLoc, a unified end-to-end framework for timestamp localization that can handle multiple tasks.
  arXiv  Detail & Related papers  (2025-03-09T09:11:26Z)
• Enhancing Temporal Modeling of Video LLMs via Time Gating [38.86742466948778]
  Video Large Language Models (Video LLMs) have achieved impressive performance on video-and-language tasks, such as video question answering. Most existing Video LLMs neglect temporal information in video data, leading to struggles with temporal-aware video understanding. We propose a Time Gating Video LLM (TG-Vid) designed to enhance temporal modeling through a novel Time Gating module (TG)
  arXiv  Detail & Related papers  (2024-10-08T06:21:29Z)
• LITA: Language Instructed Temporal-Localization Assistant [71.68815100776278]
  We introduce time tokens that encode timestamps relative to the video length to better represent time in videos. We also introduce SlowFast tokens in the architecture to capture temporal information at fine temporal resolution. We show that our emphasis on temporal localization also substantially improves video-based text generation compared to existing Video LLMs.
  arXiv  Detail & Related papers  (2024-03-27T22:50:48Z)
• VTimeLLM: Empower LLM to Grasp Video Moments [43.51980030572101]
  Large language models (LLMs) have shown remarkable text understanding capabilities. Video LLMs can only provide a coarse description of the entire video. We propose VTimeLLM, a novel Video LLM for fine-grained video moment understanding.
  arXiv  Detail & Related papers  (2023-11-30T10:49:56Z)
• Scanning Only Once: An End-to-end Framework for Fast Temporal Grounding in Long Videos [60.86880787242561]
  Video temporal grounding aims to pinpoint a video segment that matches the query description. We propose an end-to-end framework for fast temporal grounding, which is able to model an hours-long video with textbfone-time network execution. Our method significantly outperforms state-of-the-arts, and achieves textbf14.6$times$ / textbf102.8$times$ higher efficiency respectively.
  arXiv  Detail & Related papers  (2023-03-15T03:54:43Z)
• Language-free Training for Zero-shot Video Grounding [50.701372436100684]
  Video grounding aims to localize the time interval by understanding the text and video simultaneously. One of the most challenging issues is an extremely time- and cost-consuming annotation collection. We present a simple yet novel training framework for video grounding in the zero-shot setting.
  arXiv  Detail & Related papers  (2022-10-24T06:55:29Z)
• Fine-grained Semantic Alignment Network for Weakly Supervised Temporal Language Grounding [148.46348699343991]
  Temporal language grounding aims to localize a video segment in an untrimmed video based on a natural language description. Most of the existing weakly supervised methods generate a candidate segment set and learn cross-modal alignment through a MIL-based framework. We propose a novel candidate-free framework: Fine-grained Semantic Alignment Network (FSAN), for weakly supervised TLG.
  arXiv  Detail & Related papers  (2022-10-21T13:10:27Z)

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.