Time-R1: Post-Training Large Vision Language Model for Temporal Video Grounding (2503.13377v2)

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-LLMs (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.

Analysis of "TimeZero: Temporal Video Grounding with Reasoning-Guided LVLM"

The paper "TimeZero: Temporal Video Grounding with Reasoning-Guided LVLM" introduces TimeZero, a latency video-LLM (LVLM) designed to enhance the Temporal Video Grounding (TVG) task. This task demands precise localization of relevant video segments using language-based queries. The paper advances the field by incorporating a reasoning-driven approach using reinforcement learning (RL), which sets a new benchmark in terms of performance, especially on the Charades-STA dataset.

Core Contributions

The paper emphasizes how TimeZero tackles the TVG challenge by prolonging the inference process. Instead of directly regressing timestamps, TimeZero utilizes a reasoning process to understand the interplay between video and textual information better. This approach draws from recent developments in Chain-of-Thought (CoT) reasoning and post-training reinforcement learning paradigms, which have demonstrated improved reasoning capabilities in LLMs.

Methodology and Implementation

TimeZero is framed around a reinforcement learning paradigm using Group Relative Policy Optimization (GRPO). A pivotal aspect of TimeZero's methodology is the rule-based reward scheme that guides training. The introduction of a template reward and an Intersection over Union (IoU) reward ensures that the model effectively relates video and textual data, thus producing more accurate temporal groundings. These rules encourage the model to allocate computational resources more efficiently, prioritizing reasoning over direct output inferences.

Numerical Results and Comparison

Through extensive experiments, TimeZero demonstrates superior performance in both in-domain (Charades-STA) and out-of-domain (ActivityNet) video datasets, achieving state-of-the-art results on the former. For instance, on the Charades-STA dataset, TimeZero achieved an [email protected] score of 47.9, significantly outperforming other LVLMs such as VideoChat-T by a considerable margin.

It is also noteworthy that TimeZero not only surpasses specialized, computationally intensive LVLMs but also demonstrates competitive performance against traditional VLP-based models. This establishes the efficacy of the reasoning and RL-driven paradigm for complex tasks like TVG.

Implications and Future Directions

TimeZero's achievements have significant theoretical and practical implications for AI and video understanding fields. The introduction of reasoning-guided RL approaches heralds a paradigm shift in how complex, multi-modal tasks are tackled. This approach could be extended to other domains that require synergistic understanding of temporal and linguistic elements.

Future research could explore the scalability of the TimeZero framework when applied to even larger datasets or more varied video contexts. Additionally, improving input representation fidelity by aligning with traditional methods, without sacrificing the model's real-time response capabilities, remains an avenue for further enhancement.

Conclusion

The "TimeZero" paper makes a meaningful contribution by illustrating how reinforcement learning, coupled with reasoning, can significantly enhance temporal video grounding tasks. By laying down a framework that surpasses previous LVLMs and specialized models, TimeZero sets a precedent for future research in integrating deeper reasoning into multimodal models. This move towards enriched video-language understanding opens the door to developing more nuanced AI models capable of tackling increasingly sophisticated interaction tasks.