Linear-Time Global Visual Modeling without Explicit Attention
Existing research largely attributes the global sequence modeling capability of Transformers to the explicit computation of attention weights, a process that inherently incurs quadratic computational complexity. In this work, we offer a novel perspective: we demonstrate that attention can be mathematically reframed as a Multi-Layer Perceptron (MLP) equipped with dynamically predicted parameters. Through this lens, we explain attention's global modeling power not as explicit token-wise aggregation, but as an implicit process where dynamically generated parameters act as a compressed representation of the global context. Inspired by this insight, we investigate a fundamental question: can w...
| Authors | Ruize He, Dongchen Han, Gao Huang |
|---|---|
| Primary category | cs.CV |
| Published | 3 May 2026 |
| Last updated at source | 6 May 2026 |
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