Vision Transformers Need Registers(arxiv.org)
89 pointsby felineflocka day ago4 comments
  • Ameo15 hours ago
    Extremely cool!

    It's interesting and honestly encouraging that this kind of thing can be discovered and understood using just "simple linear methods" and high-level analysis of patterns in layer activations.

  • Scene_Cast2a day ago
    So basically multiple CLS tokens.

    Fwiw, I tried multiple global tokens in my chess neural net and didn't see any uplift compared to my baseline of just having one.

    • ninja3925a day ago
      Note that it's not done for performance reason but rather to generate clear feature maps.
  • bigdicta day ago
    Has this been used widely since?
    • ashvardaniana day ago
      I can't speak for the whole industry, but we used it in older UForm <https://github.com/unum-cloud/uform> and saw good adoption, especially among those deploying on the Edge, where every little trick counts. It's hard to pin down exact numbers since most deployments didn't go through Hugging Face, but at the time, these models were likely among the more widely deployed by device count.
    • kombine21 hours ago
      For example, it is used here https://github.com/facebookresearch/vggt/
    • heyitsguaya day ago
      I ran a comparison of DINOv2 with and without registers on some image embedding tasks for work; DINOv2+registers saw a performance metric bump of 2-3%. Not nothing, not transformative, worth using when the only difference for inference is the model name string you're loading.
    • godelskia day ago
      yes