Cross-Modal Video Summarization with Temporal Prompt Instruction Tuning

Core Concepts

This paper proposes a novel cross-modal video summarization framework, V2Xum-LLaMA, that unifies different video summarization tasks into a single large language model's text decoder. The framework utilizes temporal prompts and task instructions to enable task-controllable video summarization.

Abstract

The paper introduces a new large-scale cross-modal video summarization dataset called Instruct-V2Xum, which contains 30,000 diverse videos sourced from YouTube. The dataset enables the robust fine-tuning of large vision-language models for various video summarization tasks.
The proposed V2Xum-LLaMA framework takes interleaved video frames and natural language temporal prompts as input, allowing pre-trained language models to effectively process long video sequences in an end-to-end manner. This approach removes the need for task-specific layers required in previous video summarization models.
Experiments show that V2Xum-LLaMA outperforms strong baseline models on multiple video summarization benchmarks, including video-to-video, video-to-text, and video-to-video-and-text summarization tasks. The authors also propose enhanced evaluation metrics, FCLIP and Cross-FCLIP, to better assess the performance of V2V and V2VT summarization tasks.
The paper provides a comprehensive analysis of existing video summarization datasets, methods, and evaluation metrics, highlighting the limitations of current approaches and the need for larger and more diverse datasets to effectively fine-tune large language models for video summarization.

Stats

The average duration of the source videos in Instruct-V2Xum is 183 seconds.
The average length of the text summaries is 239 tokens.
The average length of the video summaries is 30 frames.
The average compression ratio is 16.39%.

Quotes

"To address these issues, we introduce Instruct-V2Xum, a cross-modal video summarization dataset featuring 30,000 diverse videos sourced from YouTube, with lengths ranging from 40 to 940 seconds and an average summarization ratio of 16.39%."
"We propose a new video summarization framework named V2Xum-LLM. V2Xum-LLM, specifically V2Xum-LLaMA in this study, is the first framework that unifies different video summarization tasks into one large language model's (LLM) text decoder and achieves task-controllable video summarization with temporal prompts and task instructions."

Key Insights Distilled From

V2Xum-LLM: Cross-Modal Video Summarization with Temporal Prompt Instruction Tuning

by Hang Hua,Yun... at arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.12353.pdf

Deeper Inquiries

How can the proposed cross-modal video summarization framework be extended to support other video understanding tasks, such as video question answering or video captioning

The proposed cross-modal video summarization framework can be extended to support other video understanding tasks by leveraging the capabilities of large language models (LLMs) for tasks such as video question answering or video captioning. For video question answering, the framework can be adapted to generate answers to questions about the content of the video by incorporating question prompts along with the video frames. The LLM can process the combined input of video frames, questions, and temporal prompts to generate accurate responses. Similarly, for video captioning, the framework can be modified to generate descriptive captions for the video content by providing appropriate prompts and instructions to the LLM. By fine-tuning the LLM on a dataset that includes video-question pairs or video-caption pairs, the framework can effectively generate responses or captions that capture the essence of the video content.

What are the potential limitations of using large language models for video summarization, and how can they be addressed in future research

Using large language models for video summarization may have potential limitations, such as the need for extensive computational resources, the risk of overfitting on limited training data, and challenges in interpreting the decisions made by the model. To address these limitations, future research can focus on techniques such as:

Data Augmentation: Increasing the diversity and volume of training data to improve the model's generalization capabilities and reduce the risk of overfitting.
Regularization Techniques: Implementing regularization methods like dropout or weight decay to prevent overfitting and improve the model's robustness.
Interpretability: Developing methods to interpret the decisions made by the LLM during the video summarization process, such as attention mechanisms or visualization techniques.
Efficient Training: Exploring strategies for efficient training of large language models, such as distributed training or knowledge distillation, to reduce computational requirements.

By addressing these potential limitations, researchers can enhance the effectiveness and reliability of using large language models for video summarization tasks.

How can the Instruct-V2Xum dataset be further expanded or diversified to better represent the wide range of video content available on the internet

To further expand and diversify the Instruct-V2Xum dataset to better represent the wide range of video content available on the internet, researchers can consider the following strategies:

Increased Dataset Size: Continuously adding more videos to the dataset from various sources to increase the diversity and coverage of different video genres and topics.
Multimodal Annotations: Enhancing the dataset with multimodal annotations, such as audio transcripts, scene descriptions, or object labels, to provide richer context for video summarization tasks.
Fine-Grained Annotations: Including fine-grained annotations for specific video elements, such as objects, actions, or emotions, to enable more detailed and accurate video summarization.
User Interaction Data: Incorporating user interaction data, such as viewer engagement metrics or feedback, to capture subjective preferences and improve the quality of video summaries based on user preferences.
Domain-Specific Expansion: Expanding the dataset to cover specific domains or industries, such as healthcare, education, or entertainment, to cater to a wider range of applications and use cases.

By implementing these strategies, the Instruct-V2Xum dataset can evolve into a comprehensive and diverse resource for training and evaluating cross-modal video summarization models on a wide variety of video content.