google-research / google-research/big_vision
Confusion on FlexiViT
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Description
Hi, thanks for bringing us such great work! I have two questions regarding the paper.
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The PI-resize method does not introduce any learnable parameter, it should be compatible with any ViT model. Therefore, we can use the PI-resize in a zero-shot manner? Then, what's the point of training the FlexiViT? I know since the patch size can be (almost) any number with PI-resize, we can transfer the knowledge of ViT-8 through distillation. But is there any difference between training a FlexiViT and using PI-resize directly in the ViT-8 model (without training)? In Figure 3, the authors mentioned that "Standard ViTs (ViT-16/ViT-30) are not flexible", but the authors "simply resize the patch embedding weights ω and the position embeddings π with bilinear interpolation", not PI.
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Will the weight of FlexiCLIP be released someday?
Thanks, I am really looking forward to the answers!
Best,
Zilun
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No file, test, or entry point is named. Start with the paper's Figure 3 and the PI-resize and FlexiCLIP references in the issue; done means clarifying the zero-shot versus trained FlexiViT distinction and whether FlexiCLIP weights will be released.
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Assessment
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 15/100