VideoLLM: Modeling Video Sequence with Large Language Models (2305.13292v2)

Abstract: With the exponential growth of video data, there is an urgent need for automated technology to analyze and comprehend video content. However, existing video understanding models are often task-specific and lack a comprehensive capability of handling diverse tasks. The success of LLMs like GPT has demonstrated their impressive abilities in sequence causal reasoning. Building upon this insight, we propose a novel framework called VideoLLM that leverages the sequence reasoning capabilities of pre-trained LLMs from NLP for video sequence understanding. VideoLLM incorporates a carefully designed Modality Encoder and Semantic Translator, which convert inputs from various modalities into a unified token sequence. This token sequence is then fed into a decoder-only LLM. Subsequently, with the aid of a simple task head, our VideoLLM yields an effective unified framework for different kinds of video understanding tasks. To evaluate the efficacy of VideoLLM, we conduct extensive experiments using multiple LLMs and fine-tuning methods. We evaluate our VideoLLM on eight tasks sourced from four different datasets. The experimental results demonstrate that the understanding and reasoning capabilities of LLMs can be effectively transferred to video understanding tasks. We release the code at https://github.com/cg1177/VideoLLM.

Overview

The paper "VideoLLM: Modeling Video Sequence with LLMs" (Chen et al., 2023 ) proposes a unified framework that integrates pre-trained LLMs with visual processing components to address diverse tasks in video understanding. The work bridges the gap between advanced sequence reasoning in NLP and complex video dynamics, presenting a modular architecture that unifies diverse modalities into a single token sequence for downstream video reasoning and prediction.

Problem Motivation and Challenges

The surge in video data has underscored the limitations of conventional, task-specific video models which tend to require bespoke architectures for individual tasks such as online action detection or dense segmentation. The paper identifies three key challenges:

• Heterogeneous Video Tasks: Conventional models are often narrowly focused and not easily extensible across tasks (e.g., action segmentation, memory retrieval, and prediction).
• Scalability and Annotation Cost: Acquiring large-scale annotated video datasets remains expensive and computationally intensive, particularly for temporal annotations.
• Leveraging LLM Capabilities: LLMs such as GPT series have demonstrated robust causal reasoning for text sequences. Translating these capabilities to video sequences is non-trivial due to the need for modality alignment between visual and linguistic representations.

By leveraging LLMs for video understanding, the framework capitalizes on the advanced sequence reasoning capabilities of decoder-only LLMs and adapts them for real-time video processing.

Methodological Innovations

Modality Encoder

The framework employs a dual encoder design to process heterogeneous inputs:

• Visual Encoder: Implements a temporal-wise unitization strategy to decompose raw video into space-time units. This encoder (e.g., methods like I3D, CLIP, SlowFast) processes video clips into feature embeddings without excessive computational overhead. By partitioning video sequences into visual units, the model maintains temporal consistency while reducing redundancy.
• Textual Processing: For scenarios where textual input is available (e.g., narration, queries), conventional tokenization schemes or using pre-trained language encoders (BERT, T5, CLIP) facilitate the extraction of high-level semantic features.

Semantic Translator

A pivotal component is the Semantic Translator, which aligns the latent visual embeddings into the language space via a linear projection. This alignment is essential:

• Semantic Bridging: The translator ensures that outputs of the frozen visual encoder are compatible with the input token sequence expected by the LLM.
• Parameter-Efficient Adaptation: Fine-tuning is performed primarily on the translator and simple task head components, leveraging parameter-efficient fine-tuning (PEFT) techniques such as LoRA, Prompt Tuning, and Prefix Tuning to reduce computational cost.

Decoder-Only Reasoner

Central to the approach is the adaptation of a decoder-only LLM for video reasoning:

• Causal Sequence Modeling: The LLM, pre-trained on large text corpora, is repurposed to perform causal inference on the unified token sequence derived from video and text encoders.
• Task-Specific Heads: Simple linear layers are appended to map the latent representations produced by the LLM to output spaces specific to tasks like online action detection, future prediction, memory retrieval, and dense event segmentation.

Experimental Setup and Results

The paper evaluates the VideoLLM framework across four widely used benchmark datasets covering eight distinct video tasks:

• Datasets: EK100, Breakfast, Ego4D, and QVHighlights.
• Tasks Evaluated: Online Action Detection, Action Anticipation, Action Segmentation, Online Captioning, Long-term Anticipation, Moment Query, Nature Language Query, and Highlight Detection.

The authors report state-of-the-art or comparable performance with significantly fewer tunable parameters. Evaluation metrics include:

• Action Recognition Tasks: Metrics like Recall Top-5, Rank@1, and Rank@5.
• Captioning and Retrieval: METEOR and ROUGE-L for captioning; mAP@IoU for moment query and highlight detection tasks.

Furthermore, the experiments validate the scalability of the framework across different sizes of LLMs (such as GPT-2, T5, OPT, and LLaMA), underscoring the efficiency of the parameter-sharing mechanism and the modular design.

Implementation Considerations

Integration with Existing Visual Encoders

For deployment, the modular design allows the integration of pre-trained visual encoders with minimal modifications. Because the visual encoder is typically frozen during fine-tuning, memory and compute costs are predominantly allocated to the semantic translator and the LLM. This architecture is particularly beneficial in scenarios where real-time processing is required, as the unidirectional flow of data aligns with online prediction tasks.

Parameter-Efficient Fine-Tuning (PEFT)

Adopting PEFT methods is essential for practical deployment:

• LoRA, Prompt Tuning, and Prefix Tuning help in adapting large LLMs to the video domain without incurring full-scale retraining.
• When implementing these techniques, practitioners should monitor trade-offs between increased inference latency (due to the additional translation module) and the benefit of enhanced cross-modal reasoning.

Computational Requirements and Scalability

• Inference Time: Given the unified framework, the real-time processing capability hinges on the efficiency of the visual encoder and the sequential decoding of the LLM. Optimizations like batch processing and caching of intermediate features (for temporal consistency) are recommended.
• Scalability: The parameter efficiency demonstrated by VideoLLM makes it suitable for deployment on resource-constrained systems; however, scaling to extremely high-resolution video data may still require distributed processing frameworks.

Practical Deployment Strategies

For real-world applications, the following steps are recommended:

1. Model Selection: Identify the appropriate pre-trained visual encoder based on the target task and dataset. The modularity of the framework allows swapping between encoders like I3D or SlowFast.
2. Unified Token Sequence Construction: Implement the modality encoder and semantic translator to create a consistent token stream that the LLM can process.
3. Fine-Tuning Strategy: Utilize a combination of basic tuning and PEFT, focusing on the translator and output task heads while optionally tuning selective layers in the LLM for improved domain adaptation.
4. Task-Specific Adaptation: Design task heads that translate the LLM’s output into actionable predictions. For online tasks, ensure low-latency retrieval; for dense prediction, optimize for spatial-temporal precision.
5. Evaluation and Metric Optimization: Benchmark the performance across multiple standard metrics to validate the unified approach against traditional task-specific models.

Conclusion

The VideoLLM framework represents a comprehensive approach for the adaptation of LLMs to video sequence modeling tasks. By effectively harmonizing visual and textual modalities through a carefully designed encoder-decoder architecture, the framework demonstrates compelling performance across diverse video understanding tasks. The modular nature of the design not only facilitates the integration of off-the-shelf visual encoders and LLMs but also supports parameter-efficient adaptation, making it a viable solution for scalable video analysis in real-world applications. The work provides strong empirical evidence and detailed methodological innovations that are of high relevance to practitioners concerned with leveraging advanced sequence reasoning capabilities for video data processing.