One Trajectory, One Token: Grounded Video Tokenization via Panoptic Sub-object Trajectory
📰 ArXiv cs.AI
Learn how to improve video tokenization for transformer models using panoptic sub-object trajectories, reducing computational inefficiencies and token numbers
Action Steps
- Apply panoptic sub-object trajectory to video data to generate tokens
- Configure the tokenization model to use the proposed grounded video tokenization paradigm
- Test the performance of the model on long videos with camera movement
- Compare the results with existing token reduction strategies
- Implement the approach in a transformer model to scale up video analysis capabilities
Who Needs to Know This
Computer vision engineers and researchers working on video analysis and transformer models can benefit from this approach to improve model efficiency and performance
Key Insight
💡 Grounded video tokenization via panoptic sub-object trajectories can effectively reduce token numbers and improve model performance, especially in cases with camera movement
Share This
💡 Improve video tokenization with panoptic sub-object trajectories! Reduce tokens and computational inefficiencies for better transformer model performance
Key Takeaways
Learn how to improve video tokenization for transformer models using panoptic sub-object trajectories, reducing computational inefficiencies and token numbers
Full Article
Title: One Trajectory, One Token: Grounded Video Tokenization via Panoptic Sub-object Trajectory
Abstract:
arXiv:2505.23617v3 Announce Type: replace-cross Abstract: Effective video tokenization is critical for scaling transformer models for long videos. Current approaches tokenize videos using space-time patches, leading to excessive tokens and computational inefficiencies. The best token reduction strategies degrade performance and barely reduce the number of tokens when the camera moves. We introduce grounded video tokenization, a paradigm that organizes tokens based on panoptic sub-object trajecto
Abstract:
arXiv:2505.23617v3 Announce Type: replace-cross Abstract: Effective video tokenization is critical for scaling transformer models for long videos. Current approaches tokenize videos using space-time patches, leading to excessive tokens and computational inefficiencies. The best token reduction strategies degrade performance and barely reduce the number of tokens when the camera moves. We introduce grounded video tokenization, a paradigm that organizes tokens based on panoptic sub-object trajecto
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