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VTimeLLM: Empower LLM to Grasp Video Moments (2311.18445v1)

Published 30 Nov 2023 in cs.CV

Abstract: LLMs have shown remarkable text understanding capabilities, which have been extended as Video LLMs to handle video data for comprehending visual details. However, existing Video LLMs can only provide a coarse description of the entire video, failing to capture the precise start and end time boundary of specific events. In this paper, we solve this issue via proposing VTimeLLM, a novel Video LLM designed for fine-grained video moment understanding and reasoning with respect to time boundary. Specifically, our VTimeLLM adopts a boundary-aware three-stage training strategy, which respectively utilizes image-text pairs for feature alignment, multiple-event videos to increase temporal-boundary awareness, and high-quality video-instruction tuning to further improve temporal understanding ability as well as align with human intents. Extensive experiments demonstrate that in fine-grained time-related comprehension tasks for videos such as Temporal Video Grounding and Dense Video Captioning, VTimeLLM significantly outperforms existing Video LLMs. Besides, benefits from the fine-grained temporal understanding of the videos further enable VTimeLLM to beat existing Video LLMs in video dialogue benchmark, showing its superior cross-modal understanding and reasoning abilities.

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Overview of VTimeLLM: Empowering LLMs for Fine-Grained Video Moment Understanding

This paper, "VTimeLLM: Empower LLM to Grasp Video Moments," proposes an innovative approach to enhancing LLMs for video understanding. The authors introduce VTimeLLM, a novel framework designed to enable LLMs to comprehend fine-grained video moments with precise temporal reasoning capabilities. In contrast to existing Video LLMs, which typically offer only coarse descriptions of videos, the VTimeLLM framework is built around a boundary-aware three-stage training strategy that significantly improves temporal reasoning and boundary detection.

The first stage aligns visual features with an LLM's semantic space using a large-scale dataset of image-text pairs, enabling the LLM to process visual content effectively. The second stage is designed to address the scarcity of temporally annotated video datasets; it enhances the model's temporal boundary awareness through custom-designed question-answering tasks on multi-event video datasets. This stage uses a large-scale video-text dataset, ensuring the LLM can correctly identify and comprehend events within their temporal contexts. The third stage refines the model's temporal understanding and alignment with human intent by using high-quality video-instruction tuning.

The authors conducted extensive experiments to validate the effectiveness of VTimeLLM, primarily focusing on Temporal Video Grounding and Dense Video Captioning tasks. The results demonstrate that VTimeLLM outperforms existing Video LLMs, highlighting the importance of fine-grained temporal understanding not only for these tasks but also for video dialogues, showcasing superior cross-modal reasoning abilities.

Key Contributions

  • First Boundary-Aware Video LLM: VTimeLLM is introduced as the first Video LLM with explicit boundary awareness, which enables it to detect and reason about specific events within a video timeline with greater precision.
  • Three-Stage Training Strategy: The proposed boundary-aware training strategy is pivotal. It consecutively leverages:
    • Large-scale image-text data for feature alignment.
    • Multi-event video-text data for enhanced temporal boundary awareness.
    • Instruction tuning using a high-quality dialogue dataset for improved reasoning aligned with human intentions.
  • Empirical Success: The paper provides empirical evidence that VTimeLLM surpasses existing Video LLMs in addressing fine-grained, temporal-related video tasks, thereby establishing a new benchmark for the field.

Theoretical and Practical Implications

The implications of the research are substantial for both theory and practice within the AI domain. Theoretically, VTimeLLM advances our understanding of how LLMs can be extended to handle multimodal data more effectively, particularly in integrating complex temporal dynamics from video inputs. Practically, the enhanced fine-grained video comprehension abilities of VTimeLLM can be deployed in numerous applications, such as video analytics, automated video summarization, and real-time video-based conversational agents, to name a few.

Speculations on Future Developments

Looking forward, the research on VTimeLLM paves the way for further explorations and enhancements in the understanding of multimodal data by LLMs. Future work could extend the boundary-aware training strategy to incorporate additional modalities beyond video, improving the comprehensiveness of LLMs in multi-sensory environments. Additionally, as the quality and scale of annotated video datasets continue to improve, models like VTimeLLM will likely become even more adept at understanding and reasoning over complex and nuanced video content.

Overall, this paper presents a substantial step forward in maximizing the potential of LLMs for video understanding, offering insightful methodologies and setting the groundwork for future innovations in this space.

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Authors (5)
  1. Bin Huang (56 papers)
  2. Xin Wang (1306 papers)
  3. Hong Chen (230 papers)
  4. Zihan Song (4 papers)
  5. Wenwu Zhu (104 papers)
Citations (57)
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