Efficiently modeling massive images is a long-standing challenge in machine learning. To this end, we introduce Multi-Scale Attention (MSA). MSA relies on two key ideas, (i) multi-scale representations (ii) bi-directional cross-scale communication. MSA creates O(log N) scales to represent the image across progressively coarser features and leverages cross-attention to propagate information across scales. We then introduce Atlas, a novel neural network architecture based on MSA. We demonstrate that Atlas significantly improves the compute-performance tradeoff of long-context image modeling in a high-resolution variant of ImageNet 100. At 1024px resolution, Atlas-B achieves 91.04% accuracy, comparable to ConvNext-B (91.92%) while being 4.3x faster. Atlas is 2.95x faster and 7.38% better than FasterViT, 2.25x faster and 4.96% better than LongViT. In comparisons against MambaVision-S, we find Atlas-S achieves 5%, 16% and 32% higher accuracy at 1024px, 2048px and 4096px respectively, while obtaining similar runtimes. Code for reproducing our experiments and pretrained models is available atthis https URL.
View on arXiv@article{agrawal2025_2503.12355,
title={ Atlas: Multi-Scale Attention Improves Long Context Image Modeling },
author={ Kumar Krishna Agrawal and Long Lian and Longchao Liu and Natalia Harguindeguy and Boyi Li and Alexander Bick and Maggie Chung and Trevor Darrell and Adam Yala },
journal={arXiv preprint arXiv:2503.12355},
year={ 2025 }
}