Summary of Elastictok: Adaptive Tokenization For Image and Video, by Wilson Yan et al.
ElasticTok: Adaptive Tokenization for Image and Video
by Wilson Yan, Volodymyr Mnih, Aleksandra Faust, Matei Zaharia, Pieter Abbeel, Hao Liu
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 ElasticTok method conditions on prior frames to adaptively encode a frame into a variable number of tokens, allowing for efficient video tokenization. By introducing a masking technique that drops a random number of tokens at the end of each frame’s token encoding, the approach enables dynamic allocation of tokens during inference. This allows more complex data to leverage more tokens while simpler data only requires a few tokens. The method is demonstrated to be effective in efficient token usage on images and video datasets, paving the way for future development of multimodal models, world models, and agents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ElasticTok is a new way to encode videos into words, or “tokens”. This helps computers learn from long videos more efficiently. The old way was limited by either using too few tokens (making it lose information) or too many tokens (making it take too long). ElasticTok solves this problem by looking at what came before and adjusting the number of tokens needed for each frame. It also drops some of these tokens to make the process faster. This helps computers learn better from videos and images, which can lead to more powerful AI models that can understand many types of data. |
Keywords
» Artificial intelligence » Inference » Token » Tokenization
