TOPA: Extend Large Language Models for Video Understanding via Text-Only Pre-Alignment

Wei Li, Hehe Fan, Yongkang Wong, Mohan Kankanhalli, Yi Yang·May 22, 2024

Summary

This paper presents Text-Only Pre-Alignment (TOPA), a novel method that enhances video understanding using large language models without pre-training on real video data. By generating Textual Videos (Tideos) with continuous textual frames and annotations, TOPA bridges the gap between textual and video modalities through CLIP feature extraction. The TOPA-Llama2-13B model achieves a competitive 51.0% Top-1 accuracy on the Egoschema benchmark, indicating its potential for comprehensive video understanding tasks. The study also involves the TextVid dataset, a large-scale, high-quality text-only video resource for tasks like summarization and QA, and explores the effectiveness of TOPA in various video understanding benchmarks, outperforming previous methods. The research highlights the benefits of text-based alignment and the potential for extending LLMs to handle video content more effectively.

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to extend large language models for video understanding through text-only pre-alignment . This involves enhancing video comprehension by aligning text descriptions with video content to enable advanced video capabilities encompassing recognition and reasoning skills . The focus is on addressing complex video understanding tasks through the utilization of language models . While the specific approach of text-only pre-alignment may be novel, the broader goal of enhancing video understanding using language models is not entirely new in the field of video analysis and comprehension .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis related to video understanding through the extension of large language models for video comprehension via text-only pre-alignment . The focus is on enhancing video understanding capabilities by leveraging language models to interpret and analyze video content solely based on textual descriptions . The research explores the effectiveness of these language models in bridging the gap between textual descriptions and visual content in videos, aiming to improve multi-modal contrastive representation learning .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "TOPA: Extend Large Language Models for Video Understanding via Text-Only Pre-Alignment" introduces a text-only pre-alignment framework called TOPA, designed to align Large Language Models (LLMs) with video modality without the need for training on real videos . This framework has shown impressive performance on challenging long-form video understanding benchmarks like EgoSchema, highlighting the effectiveness of a text-only approach in capturing the dynamics of long-form videos . The methodology of TOPA eliminates the necessity for expensive video-text data collection and extensive pre-training, making it more accessible for research and development in video-language understanding technologies . By enabling users to extract information from lengthy videos without detailed viewing, TOPA aims to enhance content moderation systems by efficiently detecting and mitigating inappropriate or harmful video content .

Furthermore, the paper presents a novel approach that includes data generation and text-only pre-alignment, offering potential applications across various vision-language tasks where obtaining paired vision-language data is challenging . This innovative framework not only simplifies the alignment of LLMs with video content but also opens up opportunities for researchers with limited resources to engage in cutting-edge multi-modal research, thereby diversifying perspectives and contributions in this field . TOPA's primary goal is to develop a general video-language understanding model that can interpret and manage video content effectively, particularly beneficial for platforms hosting user-generated content . The TOPA framework introduces several key characteristics and advantages compared to previous methods in video understanding:

  • Text-Only Pre-Alignment: TOPA utilizes a text-only pre-alignment approach, enabling the alignment of Large Language Models (LLMs) with video content without the need for training on actual videos . This innovative methodology eliminates the requirement for costly video-text data collection and extensive pre-training, making it more accessible for research and development in video-language understanding technologies .
  • Diverse Supervisions Generation: One significant advantage of TOPA is its ability to automatically generate diverse language-based supervisions, such as multi-choice QA pairs, using the LLM . This feature allows for the creation of specialized pre-alignment tasks that better equip LLMs for general video-language tasks, including dense captioning, multi-choice video QA, and video chat .
  • Performance Improvement: TOPA demonstrates improved performance in multi-choice video QA tasks, with TOPA-LLama2-13B achieving top accuracy surpassing GPT-4-based video agents in certain evaluation modes . The framework's text-only learning approach has shown promising results in capturing the dynamics of long-form videos, as evidenced by its performance on challenging benchmarks like EgoSchema .
  • Reduced Language Biases: TOPA addresses the issue of language biases introduced by substantial linguistic differences in choices, particularly in full video sets, by leveraging the LLM's robust contextual understanding for choice selection . This approach helps mitigate biases and enhances the model's ability to select appropriate choices based on video-question context.
  • Broader Impact and Accessibility: TOPA's methodology not only enhances video-language understanding capabilities but also lowers the barriers to entry for researchers with limited resources, inspiring engagement in cutting-edge multi-modal research and diversifying contributions in the field . The framework's text-only pre-alignment framework offers a more inclusive and diverse range of perspectives and contributions to video-language technologies .

Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

In the field of video understanding via text-only pre-alignment, several related research works and notable researchers have contributed to advancements in this area. Some noteworthy researchers include W. Dai, J. Li, D. Li, A. M. H. Tiong, J. Zhao, W. Wang, B. Li, P. N. Fung, and S. Hoi , V. W. Liang, Y. Zhang, Y. Kwon, S. Yeung, and J. Y. Zou , J. Gao, C. Sun, Z. Yang, and R. Nevatia , and J. Kim . The key solution mentioned in the paper "TOPA: Extend Large Language Models for Video Understanding via Text-Only Pre-Alignment" focuses on finetuning the pre-aligned TOPA models to study the benefits for downstream supervised learning by directly taking video features as input without cross-modal projection .


How were the experiments in the paper designed?

The experiments in the paper were designed with a focus on investigating the impact of Large Language Models (LLMs) in multi-choice video QA tasks. The experiments involved conducting tests on the EgoSchema dataset in a blind setting, where only questions and choices were provided to the LLMs . Various advanced LLMs such as Bard, GPT-4-Turbo, and Gemini-Pro-1.0 achieved accuracies ranging from 30.8% to 38.2% in the blind setting, showcasing the ability of LLMs to select correct answers based solely on textual inputs without visual cues . Additionally, the experiments explored the effectiveness of text-only pre-alignment in preparing LLMs for complex video-language tasks by leveraging specialized text-only tasks, even in scenarios where the original LLMs may have limitations . The experiments also included ablations on video frames to evaluate the performance of different models like Llama2-7B and TOPA-Llama2-13B in tasks such as NextQA on the ES Full dataset .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the TextVid dataset . The data generation process for this dataset utilized the Gemini Pro 1.0 API to create textual videos along with associated annotations . The TextVid dataset contains a large number of textual videos, diverse conditions, video titles, video captions, video events, and object names, making it a comprehensive and diverse dataset for video understanding tasks .

Regarding the code, the study does not explicitly mention whether the code used for the data generation process or the evaluation is open source. It primarily focuses on the methodology, experiments, and results related to extending large language models for video understanding via text-only pre-alignment .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that require verification. The study conducted various experiments, such as evaluating the performance of different models like LongViViT, MC-ViT-L, VideoAgent, and TOPA-LLama2 on tasks like Multi-choice Video QA . These experiments aimed to assess the impact of text-only pre-alignment and the effectiveness of large language models (LLMs) in video understanding tasks . The results demonstrated that TOPA-Llama2-13B achieved a blind accuracy of 37.5%, showcasing the potential of text-only pre-alignment in preparing LLMs for complex video-language tasks . Additionally, the study included detailed qualitative results to illustrate the advantages and limitations of TOPA across various video understanding tasks .

Moreover, the experiments involved tasks like Action Count, Moving Count, Moving Attribute, State Change, and Scene Transition, which required analyzing specific aspects of the videos and making predictions based on the content . These tasks provided a comprehensive evaluation of the models' capabilities in understanding and interpreting video content . The results of these tasks, along with the detailed annotations and descriptions provided, contribute to the scientific rigor of the study and support the hypotheses being tested .

Overall, the experiments conducted in the paper, along with the detailed analysis of the results and comparisons with other models, offer strong empirical evidence to validate the scientific hypotheses related to video understanding and the effectiveness of text-only pre-alignment in enhancing the performance of large language models in this domain . The combination of diverse tasks, thorough evaluations, and insightful qualitative results collectively strengthen the scientific foundation of the study and provide valuable insights into the advancements in video understanding through text-based approaches.


What are the contributions of this paper?

The contributions of the paper "TOPA: Extend Large Language Models for Video Understanding via Text-Only Pre-Alignment" include:

  • Introducing the TOPA framework that extends large language models for video understanding through text-only pre-alignment .
  • Demonstrating the benefits of TOPA for downstream supervised learning by directly utilizing video features as input without cross-modal projection .
  • Providing extensive qualitative results to illustrate the advantages and limitations of TOPA across various video understanding tasks .
  • Offering additional experiments and analysis in the appendices, such as discussing the impact of multi-choice video QA pre-training and cross-modal projection, along with details of the proposed TextVid dataset and benchmarks used .

What work can be continued in depth?

To delve deeper into the research presented in the document, further exploration can be conducted on the topic of video understanding through text-only pre-alignment. This involves investigating the effectiveness of aligning Large Language Models (LLMs) with video content without the need for real video training . Additionally, a detailed study can be carried out on the impact of cross-modal projection in this framework to understand its significance in enhancing video understanding tasks . Furthermore, exploring the advantages and limitations of the Text-Only Pre-Alignment (TOPA) framework across various video understanding tasks can provide valuable insights into its capabilities and areas for improvement .


Introduction
Background
Large language models without video pre-training
Gap between textual and video modalities
Objective
Novel method: TOPA
Enhance video understanding with text-based approach
Aim: Bridge the gap and compete with video-pretrained models
Method
Data Generation: Textual Videos (Tideos)
Continuous textual frames and annotations
CLIP feature extraction for alignment
TOPA-Llama2-13B Model
Model architecture and description
Egoschema benchmark performance (51.0% Top-1 accuracy)
TextVid Dataset
Large-scale, high-quality text-only video resource
Summarization and QA tasks
Evaluation
Benchmarks comparison: outperforming previous methods
Video understanding tasks performance
Advantages and Applications
Benefits of text-based alignment
Extending LLMs to video content
Potential use cases and future directions
Conclusion
Summary of findings
Implications for video understanding research
Limitations and future work suggestions
Basic info
papers
computer vision and pattern recognition
computation and language
artificial intelligence
Advanced features
Insights
What is the TextVid dataset mentioned in the study, and how does it contribute to the research?
What is the primary focus of the paper Text-Only Pre-Alignment (TOPA)?
What is the Top-1 accuracy achieved by the TOPA-Llama2-13B model on the Egoschema benchmark?
How does TOPA enhance video understanding using large language models?

TOPA: Extend Large Language Models for Video Understanding via Text-Only Pre-Alignment

Wei Li, Hehe Fan, Yongkang Wong, Mohan Kankanhalli, Yi Yang·May 22, 2024

Summary

This paper presents Text-Only Pre-Alignment (TOPA), a novel method that enhances video understanding using large language models without pre-training on real video data. By generating Textual Videos (Tideos) with continuous textual frames and annotations, TOPA bridges the gap between textual and video modalities through CLIP feature extraction. The TOPA-Llama2-13B model achieves a competitive 51.0% Top-1 accuracy on the Egoschema benchmark, indicating its potential for comprehensive video understanding tasks. The study also involves the TextVid dataset, a large-scale, high-quality text-only video resource for tasks like summarization and QA, and explores the effectiveness of TOPA in various video understanding benchmarks, outperforming previous methods. The research highlights the benefits of text-based alignment and the potential for extending LLMs to handle video content more effectively.
Mind map
Video understanding tasks performance
Benchmarks comparison: outperforming previous methods
Summarization and QA tasks
Large-scale, high-quality text-only video resource
Egoschema benchmark performance (51.0% Top-1 accuracy)
Model architecture and description
CLIP feature extraction for alignment
Continuous textual frames and annotations
Aim: Bridge the gap and compete with video-pretrained models
Enhance video understanding with text-based approach
Novel method: TOPA
Gap between textual and video modalities
Large language models without video pre-training
Limitations and future work suggestions
Implications for video understanding research
Summary of findings
Potential use cases and future directions
Extending LLMs to video content
Benefits of text-based alignment
Evaluation
TextVid Dataset
TOPA-Llama2-13B Model
Data Generation: Textual Videos (Tideos)
Objective
Background
Conclusion
Advantages and Applications
Method
Introduction
Outline
Introduction
Background
Large language models without video pre-training
Gap between textual and video modalities
Objective
Novel method: TOPA
Enhance video understanding with text-based approach
Aim: Bridge the gap and compete with video-pretrained models
Method
Data Generation: Textual Videos (Tideos)
Continuous textual frames and annotations
CLIP feature extraction for alignment
TOPA-Llama2-13B Model
Model architecture and description
Egoschema benchmark performance (51.0% Top-1 accuracy)
TextVid Dataset
Large-scale, high-quality text-only video resource
Summarization and QA tasks
Evaluation
Benchmarks comparison: outperforming previous methods
Video understanding tasks performance
Advantages and Applications
Benefits of text-based alignment
Extending LLMs to video content
Potential use cases and future directions
Conclusion
Summary of findings
Implications for video understanding research
Limitations and future work suggestions

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to extend large language models for video understanding through text-only pre-alignment . This involves enhancing video comprehension by aligning text descriptions with video content to enable advanced video capabilities encompassing recognition and reasoning skills . The focus is on addressing complex video understanding tasks through the utilization of language models . While the specific approach of text-only pre-alignment may be novel, the broader goal of enhancing video understanding using language models is not entirely new in the field of video analysis and comprehension .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis related to video understanding through the extension of large language models for video comprehension via text-only pre-alignment . The focus is on enhancing video understanding capabilities by leveraging language models to interpret and analyze video content solely based on textual descriptions . The research explores the effectiveness of these language models in bridging the gap between textual descriptions and visual content in videos, aiming to improve multi-modal contrastive representation learning .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "TOPA: Extend Large Language Models for Video Understanding via Text-Only Pre-Alignment" introduces a text-only pre-alignment framework called TOPA, designed to align Large Language Models (LLMs) with video modality without the need for training on real videos . This framework has shown impressive performance on challenging long-form video understanding benchmarks like EgoSchema, highlighting the effectiveness of a text-only approach in capturing the dynamics of long-form videos . The methodology of TOPA eliminates the necessity for expensive video-text data collection and extensive pre-training, making it more accessible for research and development in video-language understanding technologies . By enabling users to extract information from lengthy videos without detailed viewing, TOPA aims to enhance content moderation systems by efficiently detecting and mitigating inappropriate or harmful video content .

Furthermore, the paper presents a novel approach that includes data generation and text-only pre-alignment, offering potential applications across various vision-language tasks where obtaining paired vision-language data is challenging . This innovative framework not only simplifies the alignment of LLMs with video content but also opens up opportunities for researchers with limited resources to engage in cutting-edge multi-modal research, thereby diversifying perspectives and contributions in this field . TOPA's primary goal is to develop a general video-language understanding model that can interpret and manage video content effectively, particularly beneficial for platforms hosting user-generated content . The TOPA framework introduces several key characteristics and advantages compared to previous methods in video understanding:

  • Text-Only Pre-Alignment: TOPA utilizes a text-only pre-alignment approach, enabling the alignment of Large Language Models (LLMs) with video content without the need for training on actual videos . This innovative methodology eliminates the requirement for costly video-text data collection and extensive pre-training, making it more accessible for research and development in video-language understanding technologies .
  • Diverse Supervisions Generation: One significant advantage of TOPA is its ability to automatically generate diverse language-based supervisions, such as multi-choice QA pairs, using the LLM . This feature allows for the creation of specialized pre-alignment tasks that better equip LLMs for general video-language tasks, including dense captioning, multi-choice video QA, and video chat .
  • Performance Improvement: TOPA demonstrates improved performance in multi-choice video QA tasks, with TOPA-LLama2-13B achieving top accuracy surpassing GPT-4-based video agents in certain evaluation modes . The framework's text-only learning approach has shown promising results in capturing the dynamics of long-form videos, as evidenced by its performance on challenging benchmarks like EgoSchema .
  • Reduced Language Biases: TOPA addresses the issue of language biases introduced by substantial linguistic differences in choices, particularly in full video sets, by leveraging the LLM's robust contextual understanding for choice selection . This approach helps mitigate biases and enhances the model's ability to select appropriate choices based on video-question context.
  • Broader Impact and Accessibility: TOPA's methodology not only enhances video-language understanding capabilities but also lowers the barriers to entry for researchers with limited resources, inspiring engagement in cutting-edge multi-modal research and diversifying contributions in the field . The framework's text-only pre-alignment framework offers a more inclusive and diverse range of perspectives and contributions to video-language technologies .

Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

In the field of video understanding via text-only pre-alignment, several related research works and notable researchers have contributed to advancements in this area. Some noteworthy researchers include W. Dai, J. Li, D. Li, A. M. H. Tiong, J. Zhao, W. Wang, B. Li, P. N. Fung, and S. Hoi , V. W. Liang, Y. Zhang, Y. Kwon, S. Yeung, and J. Y. Zou , J. Gao, C. Sun, Z. Yang, and R. Nevatia , and J. Kim . The key solution mentioned in the paper "TOPA: Extend Large Language Models for Video Understanding via Text-Only Pre-Alignment" focuses on finetuning the pre-aligned TOPA models to study the benefits for downstream supervised learning by directly taking video features as input without cross-modal projection .


How were the experiments in the paper designed?

The experiments in the paper were designed with a focus on investigating the impact of Large Language Models (LLMs) in multi-choice video QA tasks. The experiments involved conducting tests on the EgoSchema dataset in a blind setting, where only questions and choices were provided to the LLMs . Various advanced LLMs such as Bard, GPT-4-Turbo, and Gemini-Pro-1.0 achieved accuracies ranging from 30.8% to 38.2% in the blind setting, showcasing the ability of LLMs to select correct answers based solely on textual inputs without visual cues . Additionally, the experiments explored the effectiveness of text-only pre-alignment in preparing LLMs for complex video-language tasks by leveraging specialized text-only tasks, even in scenarios where the original LLMs may have limitations . The experiments also included ablations on video frames to evaluate the performance of different models like Llama2-7B and TOPA-Llama2-13B in tasks such as NextQA on the ES Full dataset .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the TextVid dataset . The data generation process for this dataset utilized the Gemini Pro 1.0 API to create textual videos along with associated annotations . The TextVid dataset contains a large number of textual videos, diverse conditions, video titles, video captions, video events, and object names, making it a comprehensive and diverse dataset for video understanding tasks .

Regarding the code, the study does not explicitly mention whether the code used for the data generation process or the evaluation is open source. It primarily focuses on the methodology, experiments, and results related to extending large language models for video understanding via text-only pre-alignment .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that require verification. The study conducted various experiments, such as evaluating the performance of different models like LongViViT, MC-ViT-L, VideoAgent, and TOPA-LLama2 on tasks like Multi-choice Video QA . These experiments aimed to assess the impact of text-only pre-alignment and the effectiveness of large language models (LLMs) in video understanding tasks . The results demonstrated that TOPA-Llama2-13B achieved a blind accuracy of 37.5%, showcasing the potential of text-only pre-alignment in preparing LLMs for complex video-language tasks . Additionally, the study included detailed qualitative results to illustrate the advantages and limitations of TOPA across various video understanding tasks .

Moreover, the experiments involved tasks like Action Count, Moving Count, Moving Attribute, State Change, and Scene Transition, which required analyzing specific aspects of the videos and making predictions based on the content . These tasks provided a comprehensive evaluation of the models' capabilities in understanding and interpreting video content . The results of these tasks, along with the detailed annotations and descriptions provided, contribute to the scientific rigor of the study and support the hypotheses being tested .

Overall, the experiments conducted in the paper, along with the detailed analysis of the results and comparisons with other models, offer strong empirical evidence to validate the scientific hypotheses related to video understanding and the effectiveness of text-only pre-alignment in enhancing the performance of large language models in this domain . The combination of diverse tasks, thorough evaluations, and insightful qualitative results collectively strengthen the scientific foundation of the study and provide valuable insights into the advancements in video understanding through text-based approaches.


What are the contributions of this paper?

The contributions of the paper "TOPA: Extend Large Language Models for Video Understanding via Text-Only Pre-Alignment" include:

  • Introducing the TOPA framework that extends large language models for video understanding through text-only pre-alignment .
  • Demonstrating the benefits of TOPA for downstream supervised learning by directly utilizing video features as input without cross-modal projection .
  • Providing extensive qualitative results to illustrate the advantages and limitations of TOPA across various video understanding tasks .
  • Offering additional experiments and analysis in the appendices, such as discussing the impact of multi-choice video QA pre-training and cross-modal projection, along with details of the proposed TextVid dataset and benchmarks used .

What work can be continued in depth?

To delve deeper into the research presented in the document, further exploration can be conducted on the topic of video understanding through text-only pre-alignment. This involves investigating the effectiveness of aligning Large Language Models (LLMs) with video content without the need for real video training . Additionally, a detailed study can be carried out on the impact of cross-modal projection in this framework to understand its significance in enhancing video understanding tasks . Furthermore, exploring the advantages and limitations of the Text-Only Pre-Alignment (TOPA) framework across various video understanding tasks can provide valuable insights into its capabilities and areas for improvement .

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