VideoMind: An All-in-One Video AI Tool for Automated Production, Long-Video Reasoning, and Video Summarization 199

What is VideoMind?

VideoMind is a video AI tool that integrates video AI model automated production, long video reasoning, and video summarization. It is based on process-based operations with a high degree of automation, comes with massive built-in data, and can quickly train high-quality models. In addition, it achieves long video reasoning through a Chain-of-LoRA strategy, accurately locating and generating answers. Moreover, it can generate video summaries with one click, helping users quickly extract key information and improving video content processing efficiency.

VideoMind Features

• Comprehensive functionality: It integrates all functions from model creation to model publishing, including 7 stages: creating a model, creating tags, uploading data, annotating data, training the model, evaluation and verification, and publishing the model.

• Rich data: It integrates up to millions of hours of annotated data, and comes with massive built-in topic tags and entity tags. When users create a tag that successfully maps to a built-in tag, they no longer need to upload or annotate data for that tag.

• Efficient production: On one hand, the automated production process lowers the technical threshold, saving customers development time; on the other hand, the built-in pre-trained AI network architecture and parameters enable customers to quickly train and iterate models.

• High model quality: Thanks to Baidu's years of accumulation in the AI field, the models produced by VideoMind deliver excellent results and can support enterprises' vertical video and image content analysis needs.

• Flexible deployment: Trained models can be deployed to public clouds, private clouds, embedded devices, etc., and customers can then invoke them via API.

• One-click summary generation: It can generate video summaries, translations, and formatted outputs for social posts with one click.

• Long video reasoning capability: Through a role-based workflow (including planner, grounder, verifier, and answerer) and the Chain-of-LoRA strategy, it achieves precise temporal grounding and reasoning on long videos, efficiently handling complex video understanding tasks.
  

• Quickly extract key information: It helps users quickly and accurately analyze and summarize key information from YouTube videos, saving time and effort.

Chain-of-LoRA Agents for Long Video Reasoning

• Role-based workflow: Four roles are defined to collaborate with each other for long video reasoning, namely planner, grounder, verifier, and answerer.

• Planner: Responsible for dynamically coordinating the calling order of other roles based on the query.

• Grounder: Responsible for precisely locating relevant moments in the video based on text queries.

• Verifier: Responsible for verifying the accuracy of the grounder's results.

• Answerer: Responsible for generating the final natural language answer.

• Chain-of-LoRA strategy: Based on a vision-language model, it dynamically activates role-specific LoRA adapters during inference, efficiently integrating the above roles.

• Outstanding performance: It performs excellently across multiple public benchmarks, including detailed video question answering tasks, video temporal grounding tasks, and general video question answering tasks.

VideoMind Application Scenarios

• Education: Students can quickly obtain the key content of course videos to assist learning and review.

• Business analysis: Enterprises can summarize market research videos to quickly obtain industry trends and competitor information.

• Content creation: Video creators can extract the highlights from their own videos to produce promotional materials or social media content.

• Entertainment consumption: When watching long videos, users can quickly understand the main content to help decide whether to watch the full video.

• Social media: On short video platforms, users can extract highlights from videos to create engaging short video content.

Related Links

Project Page:https://videomind.github.io/

Github Repository:https://github.com/yeliudev/VideoMind

Demo:https://huggingface.co/spaces/yeliudev/VideoMind-2B

Paper:https://arxiv.org/abs/2503.13444