LLaVA-Video

Research on video instruction tuning and synthetic data.

CommonProductVideoVideo UnderstandingMultimodal Learning

LLaVA-Video is a large multimodal model (LMM) focused on video instruction tuning, addressing the challenge of acquiring high-quality raw data from the internet by creating a high-quality synthetic dataset, LLaVA-Video-178K. This dataset includes detailed video descriptions, open-ended questions, and multiple-choice questions, aimed at enhancing the understanding and reasoning capabilities of video language models. The LLaVA-Video model has demonstrated outstanding performance across various video benchmarks, validating the effectiveness of its dataset.

Video instruction tuning: Training improves the instruction-following capabilities of video language models using the synthetic dataset LLaVA-Video-178K.
Multitasking: The dataset covers various task types including video description
open-ended questions
and multiple-choice questions.
High-quality data synthesis: Leveraging GPT-4o to generate detailed video descriptions and diverse pairs of questions and answers.
Video representation optimization: Utilizing the SlowFast video representation method to balance frame count and visual features
enhancing GPU resource utilization.
Cross-dataset performance enhancement: Combining existing visual instruction tuning data with training on the LLaVA-Video-178K dataset to bolster model performance across multiple video benchmarks.
Open-source resources: Providing datasets
generation processes

\The target audience includes researchers
developers working on video understanding and multimodal studies
as well as companies interested in video language models. The high-quality synthetic dataset and advanced video representation methods provided by LLaVA-Video can assist them in building and optimizing more accurate and efficient video understanding models
promoting the development of video analysis and multimodal interaction technologies.\

Researchers utilizing the LLaVA-Video dataset to train a custom video language model to enhance performance in video question-answering tasks.
Developers employing the LLaVA-Video model API to implement video content analysis features for mobile applications
such as video search and recommendations.
Companies adopting the LLaVA-Video model for video content moderation
automatically identifying and filtering inappropriate content to improve content management efficiency.

1. Visit the official LLaVA-Video website or GitHub page to learn about the project background and model features.
2. Download the LLaVA-Video-178K dataset along with the corresponding model checkpoints.
3. Set up the experimental environment based on the provided Training Code
including installing necessary dependencies and configuring hardware resources.
4. Pre-train or fine-tune the LLaVA-Video model using the dataset to adapt it to specific video understanding and analysis tasks.

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