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.

  1. 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.

  2. Will the weight of FlexiCLIP be released someday?

Thanks, I am really looking forward to the answers!

Best,

Zilun

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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.

Written by the indexing model from the issue text.

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

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