VideoLLM 知道何時發聲：透過影片-文字二重互動格式增強時效性影片理解
VideoLLM Knows When to Speak: Enhancing Time-Sensitive Video Comprehension with Video-Text Duet Interaction Format
摘要
Summary
AI-Generated Summary
Paper Overview
This paper introduces the MMDuet model for video-text interaction tasks, utilizing a video-text duet format for real-time response generation. The MMDuetIT dataset is created to train VideoLLMs in this format, significantly improving performance on time-sensitive tasks like video captioning and highlight detection.
Core Contribution
- Introduction of the video-text duet interaction format for VideoLLMs.
- Development of the MMDuet model with additional informative and relevance heads for response generation.
- Creation of the MMDuetIT dataset to train VideoLLMs in the video-text duet format.
- Proposal of the MAGQA task to benchmark VideoLLMs' real-time response capabilities.
Research Context
The study positions itself within the realm of video comprehension systems, focusing on enhancing real-time response abilities through the video-text duet interaction format. It addresses the limitations of existing VideoLLMs by introducing a novel model structure and training dataset for improved performance in time-sensitive video tasks.
Keywords
Video Large Language Models (VideoLLMs), MMDuet model, MMDuetIT dataset, MAGQA task, real-time response, video-text duet interaction, informative head, relevance head, time-sensitive tasks
Background
The research background of this paper lies in the inadequacy of existing VideoLLMs in addressing user-model interaction formats for real-time responses. The study aims to bridge this gap by introducing the video-text duet interaction format and the MMDuet model, focusing on enhancing performance in time-sensitive video tasks.
Research Gap
Existing VideoLLMs lack efficient user-model interaction formats for real-time responses. Prior approaches focus on model architectures and training datasets, neglecting timely interactions. Technical Challenges Incorporating real-time response capabilities in VideoLLMs. Addressing time-sensitive tasks like video captioning and highlight detection. Prior Approaches Existing VideoLLMs emphasize model architectures and training datasets. Neglect of user-model interaction formats for real-time responses.
Methodology
The research methodology involves developing the MMDuet model with a visual encoder, projector, and transformer-decoder LLM, incorporating informative and relevance heads for response generation. The MMDuetIT dataset is utilized for training, encompassing tasks like dense captioning, multi-answer grounded video question-answering, and temporal video grounding.
Theoretical Foundation
Utilization of transformer-decoder LLM for response generation. Inclusion of informative and relevance heads to enhance response quality. Technical Architecture MMDuet model structure with visual encoder, projector, and transformer-decoder LLM. Implementation Details Training tasks include dense captioning, multi-answer grounded video question-answering, and temporal video grounding. Innovation Points Introduction of informative and relevance heads in the MMDuet model. Utilization of the video-text duet format for real-time response generation.
Experimental Validation
The experimental validation involves evaluating MMDuet's performance in tasks like highlight detection and temporal video grounding, showcasing significant improvements over baseline models like TimeChat and VTimeLLM. The model demonstrates robustness in dense video captioning tasks and excels in real-time response generation for the MAGQA task.
Setup
Training on the MMDuetIT dataset with tasks like dense captioning and multi-answer grounded video question-answering. Metrics Evaluation based on CIDEr and CODA c metrics for text quality. Results Significant improvements in highlight detection and temporal video grounding tasks. Comparative Analysis Outperformance of baseline models in text quality and real-time response generation.
Impact and Implications
The study's key findings include the effectiveness of the MMDuet model in time-sensitive video tasks and real-time response generation. While acknowledging limitations in hyperparameter requirements and future frame information incorporation, the research suggests practical applications in enhancing video comprehension systems through the video-text duet interaction format.
Key Findings
Significant improvements in time-sensitive tasks and real-time response generation. Limitations Hyperparameter requirements during inference and the need for future frame information incorporation. Future Directions Addressing inference speed and collecting real-time response datasets. Practical Significance Enhancing video comprehension systems through the video-text duet interaction format.