Video Summarization with Large Language Models

Generative illustration for Video Summarization with Large Language Models

LLM-Based Video Summarization

The exponential growth of online video content creates a critical need for advanced and efficient video summarization techniques.
Traditional summarization methods rely heavily on visual features and temporal dynamics, often failing to capture deeper video semantics.
The proposed LLMVS framework leverages Large Language Models to evaluate video frames in alignment with human judgment and diverse semantics.
The method translates video frames into captions using a Multi-modal Large Language Model and assesses frame importance with an LLM.
Local importance scores are refined using a global attention mechanism to ensure summaries reflect both local details and the overarching narrative.

Existing video summarization methods, which heavily rely on visual features and temporal dynamics, often fail to capture the semantics of video content, resulting in incomplete or incoherent summaries.

LLM-Based Video Summarization

Large Language Models offer new opportunities for video summarization by providing strong contextual understanding and cross-domain reasoning.
The authors introduce LLMVS, a framework that uses multi-modal LLMs to generate textual descriptions of video frames.
A local-to-global model evaluates frame importance by processing local windows and applying self-attention to aggregate global context.
Experimental results demonstrate that extracting output embeddings from LLMs is more effective than relying on their direct generated answers.
The proposed method achieves state-of-the-art performance on benchmark datasets like SumMe and TVSum.
Prior video summarization research has evolved from LSTM-based temporal modeling to sophisticated self-attention mechanisms and localization components.

Unlike existing methods that rely on the end output of LLMs, our method extracts the output embeddings from LLMs and apply self-attention on them to aggregate global context from the videos and make the final predictions.

Multimodal Video Summarization Approaches

Traditional video summarization maps spatiotemporal dependencies using visual cues and temporal features.
Prior multimodal methods integrate visual and textual features but typically treat text as auxiliary information.
The proposed LLMVS framework leverages large language models to incorporate rich semantic information and general knowledge.
The architecture consists of text description generation, local importance scoring, and global context aggregation components.
Frame captions are generated via a pre-trained Multi-modal Large Language Model and processed using in-context learning prompts.

While these approaches incorporate language to enhance semantic understanding, they primarily focus on refining visual representations, often treating textual information as auxiliary information.

LLM-based Video Summarization Framework

Multimodal large language models convert visual features into text space to enhance reasoning across complex domains.
The proposed LLMVS framework consists of text description generation, local important frame scoring, and global context aggregation.
Video summarization aims to assign an importance score to each frame of a given video sequence.
A pre-trained M-LLM generates detailed one-sentence captions for each individual video frame.
A pre-trained LLM evaluates local frame importance using a sliding window and in-context learning to filter out repetitive information.

Due to the inherent redundancy in video frames, it is essential to identify key frames based on local context rather than individual frames to filter out repetitive information.

LLM Video Summarization

The method utilizes in-context learning with prompts containing instructions, examples, and queries to evaluate frame importance.
Prompts feed window-specific frame captions into the LLM to assess a central frame relative to its surrounding context.
Output embeddings are extracted from the Llama-2 model after the RMS Norm layer instead of using direct textual answers.
Query and answer embeddings preserve richer contextual and semantic information about local frames.
Self-attention blocks are applied to aggregate global context across the entire video for final scoring.

Rather than obtaining direct answers from the LLM, we extract and utilize output embeddings in video summarization, which retain richer contextual and semantic information from the internal representations.

Video Summarization Methodology and Evaluation

Input embeddings are processed through global attention blocks to produce final importance scores for the video.
The training objective utilizes the Mean Squared Error loss function to optimize frame importance predictions against ground truth scores.
Experiments are conducted using two well-known benchmark datasets, SumMe and TVSum, to evaluate method performance.
Evaluation protocols adapt differently to each dataset, converting frame scores to video summaries via Kernel Temporal Segmentation for SumMe while comparing scores individually for TVSum.
Kendall's tau and Spearman's rho are adopted as rank order statistics metrics, purposefully excluding the F1 score due to its bias toward short shots.

While the F1 score is widely used in video summarization, it favors short shots over key shots due to length constraints.

LLMVS Model Performance Evaluation

Table 1 compares the performance of LLM and LLMVS models against state-of-the-art methods across two benchmark datasets, categorized by baseline, visual, and multimodal approaches.
Evaluating LLMs in a zero-shot setting demonstrates competitive performance on SumMe, but lower performance on TVSum due to differing dataset evaluation protocols.
The performance discrepancy reveals that standard LLMs are well-suited for general summarization tasks but struggle to capture individual user preferences.
The proposed LLMVS model achieves state-of-the-art performance on both benchmark datasets, significantly outperforming the zero-shot LLM.
The global context aggregator enhances LLM reasoning by capturing contextual relationships across local windows for sequence-level decision-making.
Centering summarization around language rather than treating text as an auxiliary input highlights the advantage of integrating textual data with broad LLM reasoning capabilities.

This performance gap, attributed to differences in subjectivity between the two datasets, indicates that the LLM is well-suited for general summarization tasks but less effective in capturing individual user preferences.

Analysis of Finetuning and Prompting

Experiments on zero-shot and finetuned settings evaluate whether performance gains stem from architectural enhancements or benchmark-specific finetuning.
Providing captions from the M-LLM to the LLM yields better results than direct scoring, highlighting the value of language semantics.
Applying LoRA validates the effectiveness of finetuning, though the proposed LLMVS model shows significantly greater improvements.
Evaluating different prompting styles for the M-LLM reveals that generic prompts outperform central-background region descriptions.
For the LLM, direct numerical scoring of frame importance consistently outperforms textual summarization within a local window.

Comparing the first and third rows of the table demonstrates that explicitly providing captions from M-LLM to the LLM yields better results than direct scoring by M-LLM alone, underscoring the importance of leveraging language semantics in video summarization.

Ablation and Generalization Studies

Ablation studies validate the effectiveness of combining query and answer embeddings with a global context aggregator for optimal performance.
Using self-attention blocks yields better results than using a simple multi-layer perceptron in the architecture.
Embeddings extracted after the RMS Norm layer outperform those after the Linear layer due to retaining richer contextual information.
Zero-shot evaluation on the MR.HiSum dataset demonstrates the strong generalization capability of the proposed LLMVS method on unseen videos.

Embeddings extracted after the RMS Norm layer outperform those after the Linear layer, likely due to their retention of richer contextual information, whereas embeddings after the Linear layer are more specialized for word domains.

Qualitative Results and Conclusion

Figure 5 displays qualitative results on the TVSum dataset comparing LLMVS predictions against ground-truth user scores.
The predicted importance scores closely align with overall ground truth trends, demonstrating the model's robustness and generalization.
Action-related scenes consistently receive higher importance scores from both human annotators and the LLMVS model.
Static scenes like talking to the camera or interviews receive lower scores compared to dynamic actions like stunts or riding.
The LLM-based Video Summarization framework successfully leverages large language models for caption-guided frame scoring.
Experimental results on SumMe and TVSum show consistent improvements, paving the way for advanced cross-modal multimedia analysis.

These patterns suggest that LLMVS effectively identifies and emphasizes action-oriented content that contributes significantly to the narrative.

Academic References and Bibliography

The text consists exclusively of academic citations and bibliographic references related to computer science and machine learning.
Major language models such as GPT-4, PaLM 2, and Falcon are cited through their respective technical reports.
Multimodal and visual language models, including Flamingo and Palm-E, are heavily represented in the reference list.
Research papers focusing on video summarization techniques, datasets, and attention mechanisms constitute a significant portion of the citations.
Open-source initiatives and community models like Vicuna are also documented among the technical preprints.

Gpt-4 technical report.

Academic References and Bibliography

The text consists of academic citations pointing to research papers in computer vision and machine learning.
Several entries focus on video summarization, captioning, and moment localization techniques.
References include prominent works on vision-language pre-training models like BLIP and visual instruction tuning.
Multiple citations cover modular reasoning and question-answering systems applied to video data.
Foundational machine learning papers regarding optimization and transformer architectures are also listed.

Video recap: Recursive captioning of hour-long videos.

Academic References Bibliography

The text consists of academic citations referencing research papers on machine learning and computer vision.
Several entries focus on video summarization and highlight detection techniques using neural networks.
Foundational models and instruction-following language models like Llama and Alpaca are cited.
Architectures involving attention mechanisms and memory networks for handling long documents or videos are highlighted.
Preprint and conference papers span multiple years, illustrating rapid advancements in multimodal AI systems.

Attention is all you need.

Academic References in Computer Vision

The text provides academic citations focusing on computer vision and video processing research.
Several papers discuss innovative approaches to video summarization using neural networks and adversarial architectures.
Recent entries highlight the integration of large language models for tasks like video anomaly detection and action anticipation.
Methodologies include set-based learning, recurrent neural networks, and sequence-graph networks for analyzing video content.

Harnessing large language models for training-free video anomaly detection.

Kept 7 of 14 sections, 1 of 5 figures.

LLM-Based Video Summarization

The proposed LLMVS framework leverages Large Language Models to evaluate video frames in alignment with human judgment and diverse semantics.

Existing video summarization methods, which heavily rely on visual features and temporal dynamics, often fail to capture the semantics of video content, resulting in incomplete or incoherent summaries.

LLM-Based Video Summarization

Experimental results demonstrate that extracting output embeddings from LLMs is more effective than relying on their direct generated answers.

Unlike existing methods that rely on the end output of LLMs, our method extracts the output embeddings from LLMs and apply self-attention on them to aggregate global context from the videos and make the final predictions.

LLM-based Video Summarization Framework

A pre-trained LLM evaluates local frame importance using a sliding window and in-context learning to filter out repetitive information.

Due to the inherent redundancy in video frames, it is essential to identify key frames based on local context rather than individual frames to filter out repetitive information.

LLM Video Summarization

Output embeddings are extracted from the Llama-2 model after the RMS Norm layer instead of using direct textual answers.

Rather than obtaining direct answers from the LLM, we extract and utilize output embeddings in video summarization, which retain richer contextual and semantic information from the internal representations.

LLMVS Model Performance Evaluation

The performance discrepancy reveals that standard LLMs are well-suited for general summarization tasks but struggle to capture individual user preferences.

This performance gap, attributed to differences in subjectivity between the two datasets, indicates that the LLM is well-suited for general summarization tasks but less effective in capturing individual user preferences.

Analysis of Finetuning and Prompting

Providing captions from the M-LLM to the LLM yields better results than direct scoring, highlighting the value of language semantics.

Comparing the first and third rows of the table demonstrates that explicitly providing captions from M-LLM to the LLM yields better results than direct scoring by M-LLM alone, underscoring the importance of leveraging language semantics in video summarization.

Ablation and Generalization Studies

Embeddings extracted after the RMS Norm layer outperform those after the Linear layer due to retaining richer contextual information.

Embeddings extracted after the RMS Norm layer outperform those after the Linear layer, likely due to their retention of richer contextual information, whereas embeddings after the Linear layer are more specialized for word domains.