Papers › Linear Attention with Global Context: A Multipole Attention Mechanism for Vision and Physics
Linear Attention with Global Context: A Multipole Attention Mechanism for Vision and Physics
Alex Colagrande, Paul Caillon, Eva Feillet, Alexandre Allauzen
Transformers have become the de facto standard for a wide range of tasks, from image classification to physics simulations. Despite their impressive performance, the quadratic complexity of standard Transformers in both memory and time with respect to the input length makes them impractical for processing high-resolution inputs. Therefore, several variants have been proposed, the most successful relying on patchification, downsampling, or coarsening techniques, often at the cost of losing the finest-scale details. In this work, we take a different approach. Inspired by state-of-the-art techniques in n-body numerical simulations, we cast attention as an interaction problem between grid points. We introduce the Multipole Attention Neural Operator (MANO), which computes attention in a distance-based multiscale fashion. MANO maintains, in each attention head, a global receptive field and achieves linear time and memory complexity with respect to the number of grid points. Empirical results on image classification and Darcy flows demonstrate that MANO rivals state-of-the-art models such as ViT and Swin Transformer, while reducing runtime and peak memory usage by orders of magnitude. We open source our code for reproducibility at https://github.com/AlexColagrande/MANO.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Fine-Grained Image Classification | Oxford-IIIT Pet Dataset | MANO-tiny | Accuracy | 88.31 | #13 of 15 | Archive leaderboard | report |
| Fine-Grained Image Classification | Stanford Cars | MANO-tiny | Accuracy | 65.68 | #83 of 83 | Archive leaderboard | report |
| Image Classification | CIFAR-100 | MANO-tiny | Percentage correct | 85.08 | #69 of 211 | Archive leaderboard | report |
| Image Classification | Flowers-102 | MANO-tiny | Accuracy | 89.00 | #49 of 52 | Archive leaderboard | report |
| Image Classification | Food-101 | MANO-tiny | Accuracy (%) | 82.48 | #9 of 11 | Archive leaderboard | report |
| Image Classification | Tiny ImageNet Classification | MANO-tiny | Validation Acc | 87.52 | #8 of 23 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections