Summary of Beyond Training: Dynamic Token Merging For Zero-shot Video Understanding, by Yiming Zhang et al.
Beyond Training: Dynamic Token Merging for Zero-Shot Video Understanding
by Yiming Zhang, Zhuokai Zhao, Zhaorun Chen, Zenghui Ding, Xianjun Yang, Yining Sun
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed dynamic token merging framework, DYTO, revolutionizes zero-shot video understanding by adaptively optimizing token efficiency while preserving crucial scene details. By integrating hierarchical frame selection and bipartite token merging strategies, DYTO dynamically clusters key frames and selectively compresses token sequences to achieve a balance between computational efficiency and semantic richness. In extensive experiments across multiple benchmarks, DYTO outperforms both fine-tuned and training-free methods, setting a new state-of-the-art for zero-shot video understanding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DYTO is a new way to understand videos without needing any special training or data. It takes in videos and breaks them down into smaller parts called tokens. Then it decides which of these tokens are most important and combines them together to make the video easier to understand. This helps computers be more efficient when analyzing videos while still keeping all the important details. |
Keywords
* Artificial intelligence * Token * Zero shot
