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MoETok: Sparse Expert Routing for 1D Visual Tokenizers

Paper recorded by Signals 4 on 2026-10-30 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-30 on arXiv · recorded by Signals 4 on 2026-11-02

Category: cs.CV · 计算机视觉 · first seen 2026-11-02

Abstract

Modern visual tokenizers aim to preserve fine-grained visual details while learning high-level semantic representations for downstream generation and understanding. Query-based 1D visual tokenizers encode images into flexible latent sequences, yet how individual learnable queries gather and encode image information remains poorly understood. We reveal that these queries spontaneously develop compl

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#42 most recent of 710 cs.CV papers we have recorded · ↑ newer: GeoFlip: Benchmarking Geometry-Sensitive Physics in Video Generation · ↓ older: FoAViS: Analyzing Learned Fixation Strategies in a Foveated Active Vis
Cite this page: MoETok: Sparse Expert Routing for 1D Visual Tokenizers: the #42 most recent of 710 cs.CV papers we have recorded (as of 2026-10-30). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/moetok-sparse-expert-routing-for-1d-visual-tokenizers.html
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