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Decoding the Functional Roles of Register and High-Norm Patch Tokens in Vision Transformers

Paper recorded by Signals 4 on 2026-10-02 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-02 on arXiv · recorded by Signals 4 on 2026-10-05

Category: cs.CV · 计算机视觉 · first seen 2026-10-05

Abstract

Self-supervised Vision Transformers (ViTs), such as DINOv2, learn rich visual representations, but the functions of their internal tokens remain poorly understood. Recent architectures introduce dedicated register tokens to reduce high-norm out- lier patch tokens that emerge in background re- gions, yet the semantic and functional roles of both token types have not been fully established. In this

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#366 most recent of 756 cs.CV papers we have recorded · ↑ newer: MoSE3: Learning World-Space SE(3) at Every Pixel · ↓ older: FlowHMR: Physically Plausible Motion Capture from Video
Cite this page: Decoding the Functional Roles of Register and High-Norm Patch Tokens in Vision Transformers: the #366 most recent of 756 cs.CV papers we have recorded (as of 2026-10-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/decoding-the-functional-roles-of-register-and-high-norm-patch-tokens-in-vision-t.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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