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VTimeLLM: Empower LLM to Grasp Video Moments

Bin Huang, Xin Wang, Hong Chen, Zihan Song, Wenwu Zhu

arXiv:2311.18445v1cs.CV

TL;DR

Existing Video LLMs provide coarse video descriptions but fail to capture precise event boundaries. VTimeLLM introduces boundary-aware three-stage training, and experiments show stronger performance across fine-grained temporal video tasks and video dialogue benchmarks.

  • Problem

    Existing Video LLMs struggle to capture the precise start and end boundaries of specific events while describing videos.

  • Method

    VTimeLLM uses image-text feature alignment, multi-event video QA for boundary awareness, and high-quality dialogue instruction tuning.

  • Results

    VTimeLLM significantly outperforms existing Video LLMs across fine-grained temporal video tasks and video dialogue evaluation.

  • Takeaways & Limitations

    VTimeLLM provides fine-grained temporal understanding of multiple video events while supporting video understanding and reasoning.

  • Takeaways & Limitations

    The visual modules do not explicitly model temporal relationships between frames, relying on the LLM to capture them from sequential input embeddings.

Abstract

from arXiv · show

Large language models (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.

1. Introduction

Existing Video LLMs generally summarize videos coarsely, but struggle to align specific events with precise temporal boundaries. VTimeLLM addresses this through boundary-aware training designed for fine-grained temporal reasoning.

  • Video understanding requires jointly modeling visual details and temporal dynamics.
  • Existing Video LLMs mainly generate generic captions that miss relationships between bounded events and their moment boundaries.
  • VTimeLLM uses a visual encoder, visual adapter, and tailored LLM trained with a boundary-aware three-stage strategy.
  • The second stage uses single-turn and multi-turn QA on multi-segment videos to associate events with their time boundaries.
  • The third stage applies high-quality dialogue instruction tuning to improve temporal understanding and alignment with human intentions.

2. Related Works

Related work extends language models from images to videos, but fine-grained video understanding requires locating events and connecting them to language. VTimeLLM unifies temporal grounding and dense captioning through language-model-based video understanding.

  • Image LLMs: Image LLMs align visual and linguistic modalities using visual encoders, learned feature extraction, and multimodal instruction data.
  • Video LLMs: Video LLMs such as VideoChat, Video-LLaMA, and Video-ChatGPT extend this paradigm to multiple frames through feature alignment and instruction tuning.
  • Fine-grained video understanding: Fine-grained video understanding requires precisely locating and comprehending specific events within videos.
  • Temporal Video Grounding: Temporal Video Grounding identifies video segments corresponding to textual inputs, using proposal-based or proposal-free localization.
  • Dense Video Captioning: Dense Video Captioning jointly requires temporal localization and captioning for all events in an untrimmed video.
  • VTimeLLM unifies temporal grounding and dense captioning by aligning video segments with semantic context and supporting natural-language interaction.

3. VTimeLLM: Being Aware of Time Boundaries in Videos

VTimeLLM combines a visual encoder and adapter with boundary-aware three-stage training to represent video content in the LLM space and reason about precise moments. The stages align visual features, train temporal boundary awareness on multi-event videos, and use dialogue instruction tuning for more precise, human-aligned temporal understanding.

  • 3.1. Architecture: VTimeLLM uses a visual encoder and visual adapter to transform video information into the LLM’s text space.The model inserts the resulting video feature sequence into the LLM input at a special <video> token.
  • 3.1. Architecture: The visual adapter selects each frame’s global feature and linearly projects it into the LLM embedding space, producing an input sequence of dimension d.The visual modules do not model temporal relationships explicitly; the LLM receives sequential frame embeddings and captures those relations.
  • 3.2.1 Stage 1: Feature Alignment: Stage 1 aligns visual features with the LLM’s semantic space using image-text training, while avoiding noisy video-text data and information loss from captions.The stage trains the visual adapter with image-text pairs and the LLM’s autoregressive objective.
  • 3.2.3 Stage 3: Instruction Tuning: Stage 3 uses high-quality dialogue instruction tuning to address noisy automated labels, reduce answer overfitting, follow human instructions, and improve temporal reasoning.The framework’s three stages are feature alignment, boundary perception through temporal QA, and dialogue tuning for human intent and precise temporal understanding.

4. Experiment

Experiments evaluate VTimeLLM on temporal grounding, dense captioning, ablations of its training stages, and video dialogue. VTimeLLM outperforms same-size Video LLMs, while ablations show that each training stage contributes to temporal or dialogue performance.

  • Main Results: VTimeLLM-7B significantly outperforms same-size Video LLMs on temporal video grounding and dense video captioning.Scaling to 13B produces minor changes on ActivityNet and improves temporal grounding on Charades-STA, despite no Charades-STA training data.
  • Main Results: VideoChat and VideoLLaMA’s use of only N=8 frames makes fine-grained video understanding difficult, while Vicuna’s weak positional awareness further limits temporal comprehension.The authors argue that limited temporally annotated instruction-tuning data is insufficient to resolve this issue.
  • Ablation Study: Image-only feature alignment outperforms video-only and image-plus-video alternatives across all ablation metrics.The authors attribute this to higher-quality image datasets and reduced information loss.
  • Ablation Study: Freezing the visual adapter in later stages performs similarly to tuning it, so the authors freeze it to retain information acquired during pretraining.This conclusion is based on comparisons across the image, video, and fused-data settings.
  • Ablation Study: Reusing the Stage 2 LoRA while adding another LoRA module in Stage 3 yields superior results and preserves temporal understanding acquired earlier.The comparison is between Rows 9 and 10.
  • Ablation Study: Removing Stage 1 causes misleading ActivityNet scores, poor Charades-STA localization, and unsatisfactory dense captioning, while Stage 2 alone cannot replace preliminary temporal training.Stage 3 improves the evaluated tasks and restores the model’s ability to answer broad human dialogue questions.

5. Conclusion

VTimeLLM is a Video LLM for comprehending multiple video events and providing precise temporal boundaries. Its three-stage temporal-aware training improves temporal reasoning, and experiments show significant gains over existing Video LLMs, especially on fine-grained temporal tasks.

  • VTimeLLM comprehends multiple events within videos and provides precise temporal boundaries.
  • Its three-stage framework aligns visual features, enhances temporal awareness through multi-event video-text QA, and improves temporal reasoning through instruction tuning.
  • VTimeLLM significantly outperforms existing Video LLMs across tasks, particularly on fine-grained temporal-related video tasks.

A. More Examples

Additional video-dialogue examples demonstrate VTimeLLM’s creative generation, fine-grained event understanding, and visual reasoning capabilities.

  • Creative Task: In the creative task, VTimeLLM comprehends visual information and crafts an inspired poem.
  • Fine-grained Understanding Task: In the fine-grained understanding task, VTimeLLM identifies multiple events and specific visual content within individual events.
  • Video Reasoning Task: In the video reasoning task, VTimeLLM answers questions requiring inference from comprehensive visual understanding.
  • Creative Task: Freezing the LLM throughout all three training stages preserves its ability to engage in creative dialogue.

B. Templates and Prompts

Stage 2 converts temporally annotated events into template-based QA, while Stage 3 turns events into high-quality dialogue using sequential event symbols rather than explicit timestamps.

  • Stage 2: Stage 2 represents each event with start and end timestamps from 00 to 99 plus its textual description.
  • Stage 2: Single-turn QA is used with 20% probability for dense captioning, while multi-turn QA is used with 80% probability for event captioning or temporal grounding.
  • Stage 2: Multi-turn QA queries and answers each event individually in a dialogue using separate event-captioning or temporal-grounding tasks.
  • Stage 3: Stage 3 omits specific timestamps from dialogue-generation prompts because they may introduce errors, presenting events sequentially with symbols such as <s1> and <e1>.

C. Evaluation Process

The evaluation section describes temporal grounding and dense captioning evaluation, alongside dialogue examples spanning creative, fine-grained understanding, and video reasoning tasks.

  • The evaluation process covers temporal grounding and dense captioning for VTimeLLM and other Video LLMs.
  • Creative Task: The creative-task example evaluates whether VTimeLLM can comprehend visual information and craft an inspired poem.
  • Fine-grained Understanding Task: The fine-grained understanding example examines comprehension of multiple events and specific visual content within individual events.
  • Video Reasoning Task: The video reasoning example involves questions requiring reasoning from comprehensive visual content understanding.

C.1. Evaluation of VTimeLLM

Stage-3 instruction tuning makes VTimeLLM responsive to query phrasing, while temporal-grounding results remain stable across evaluated prompts. Detailed dense-captioning prompts perform better than the QD1 prompt.

  • Evaluation setup: Without stage 3, dense captioning uses QD1, while temporal grounding evaluates QT1, QT2, and QT3 using average IoU metrics.The performance obtained from different temporal-grounding queries is similar.
  • Query sensitivity: Stage-3 VTimeLLM demonstrates instruction-following ability, with performance varying across queries.The paper reports that adding “in detail” produces more detailed video descriptions.
  • Dense captioning: Approximately 10%: the detailed dense-captioning query outperforms QD1 across various metrics.The query requests specific activities, surroundings, interactions, and JSON-formatted event timestamps.
  • Temporal grounding: Temporal-grounding metrics remain consistently close across QT1, QT2, and QT3, and a simpler “When does Ti happen?” query achieves comparable results.For temporal grounding, the reported results average the outputs of QT1, QT2, and QT3.

C.2. Evaluation of other Video LLMs

The evaluation of other Video LLMs uses templated event descriptions, dense-captioning prompts, event-captioning prompts, temporal-grounding prompts, and instruction-dialogue examples. The methodology also formats outputs and includes video duration in queries to assess models not trained on these tasks.

  • Evaluation methodology: Other Video LLMs are evaluated with video duration D included in each query and regular expressions used to format non-JSON outputs.The formatting procedure successfully handles over 70% of outputs.
  • QA construction: Dense-captioning prompts ask models to describe events occurring during different video time segments.The prompt set includes alternative formulations requesting summaries or descriptions at various timestamps.
  • QA construction: Event-captioning prompts ask what occurred between specified start and end times, while temporal-grounding prompts ask when a named event is visible.These prompt families operationalize event description and temporal localization as QA tasks.
  • Instruction tuning: Stage-3 instruction tuning uses a prompt that generates conversations about video content, including temporal perception and reasoning questions.The prompt asks for questions about preceding or succeeding events and timestamps of actions.
  • Instruction tuning: An illustrative dialogue combines an overall video summary with a question pinpointing when a woman achieved victory twice.The example uses a sequence of timestamped events involving rock-paper-scissors, a blindfold, and an embrace.
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