VLMEvalKit Eval Results in video understanding benchmark
Leaderboard and arena of Video Generation models
Chat about videos and images
Generate a video from a text prompt
Browse robotic datasets visually
Dense Grounded Understanding of Images and Videos
Submit and view evaluations of video models
Create a video from an image and audio
Fast Text 2 Video Generator
Generate lip-synced video from video/image and audio
Generate Talking avatars from Text-to-Speech
Upload and evaluate video models
Video Gallery of Dokdo
The Open VLM Video Leaderboard is a tool designed to evaluate and compare video models based on the Video Understanding Benchmark. It provides a comprehensive platform to assess and rank video models, offering insights into their performance across various video understanding tasks. This leaderboard is part of the VLMEvalKit, a framework used for evaluating video model performance.
• Real-Time Tracking: Stay updated with the latest video model performances and rankings. • Interactive Visualizations: Explore model comparisons and benchmark results through intuitive graphs and charts. • Filtering Options: Narrow down results based on specific metrics or tasks to focus on relevant data. • Benchmarking: Compare video models against standard benchmarks and other models. • Download Results: Export performance data for offline analysis and further research.
What is the purpose of the Open VLM Video Leaderboard?
The Open VLM Video Leaderboard is designed to provide a centralized platform for evaluating and comparing video models based on video understanding tasks. It helps researchers and developers identify top-performing models and track improvements over time.
Can I submit my own video model for evaluation?
Yes, the Open VLM Video Leaderboard allows submissions of video models for evaluation. Please refer to the official documentation for submission guidelines and requirements.
How often is the leaderboard updated?
The leaderboard is updated regularly to include new models and re-evaluate existing ones. Updates are typically announced on the official platform or through associated communication channels.