Where to Attend: A Principled Vision-Centric Position Encoding with Parabolas
We propose Parabolic Position Encoding (PaPE), a parabola-based position encoding for vision modalities in attention-based architectures. Given a set of vision tokens-such as images, point clouds, videos, or event camera streams-our objective is to encode their positions while accounting for the characteristics of vision modalities. Prior works have largely extended position encodings from 1D-sequences in language to nD-structures in vision, but only with partial account of vision characteristics. We address this gap by designing PaPE from principles distilled from prior work: translation invariance, rotation invariance (PaPE-RI), distance decay, directionality, and context awareness. We evaluate PaPE on 8 datasets that span 4 modalities. We find that either PaPE or PaPE-RI achieves the top performance on 7 out of 8 datasets. Extrapolation experiments on ImageNet-1K show that PaPE extrapolates remarkably well, improving in absolute terms by up to 10.5% over the next-best position encoding. Code is available at https://github.com/DTU-PAS/parabolic-position-encoding.
69824c269084cb4f0ecb54c8
Christoffer Koo Øhrstrøm
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2026-02-04T12:30:10.536Z
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Christoffer Koo Øhrstrøm
chriskoo
user
chriskoo
69824c269084cb4f0ecb54c9
Rafael I. Cabral Muchacho
69824c269084cb4f0ecb54ca
Yifei Dong
69824c269084cb4f0ecb54cb
Filippos Moumtzidellis
69824c269084cb4f0ecb54cc
Ronja Güldenring
69824c269084cb4f0ecb54cd
Florian T. Pokorny
69824c269084cb4f0ecb54ce
Lazaros Nalpantidis
Where to Attend: A Principled Vision-Centric Position Encoding with Parabolas
We propose Parabolic Position Encoding (PaPE), a parabola-based position encoding for vision modalities in attention-based architectures. Given a set of vision tokens-such as images, point clouds, videos, or event camera streams-our objective is to encode their positions while accounting for the characteristics of vision modalities. Prior works have largely extended position encodings from 1D-sequences in language to nD-structures in vision, but only with partial account of vision characteristics. We address this gap by designing PaPE from principles distilled from prior work: translation invariance, rotation invariance (PaPE-RI), distance decay, directionality, and context awareness. We evaluate PaPE on 8 datasets that span 4 modalities. We find that either PaPE or PaPE-RI achieves the top performance on 7 out of 8 datasets. Extrapolation experiments on ImageNet-1K show that PaPE extrapolates remarkably well, improving in absolute terms by up to 10.5% over the next-best position encoding. Code is available at https://github.com/DTU-PAS/parabolic-position-encoding.
Parabolic Position Encoding (PaPE) is a novel position encoding method for vision modalities that improves upon existing approaches by incorporating translation invariance, rotation invariance, distance decay, directionality, and context awareness principles.
position encoding attention-based architectures vision modalities vision tokens translation invariance rotation invariance distance decay directionality context awareness parabolic position encoding