# Scaling Local Self-Attention for Parameter Efficient Visual Backbones Source: [Scaling Local Self-Attention for Parameter Efficient Visual Backbones]() Collection index: [llms.txt]() Data updated: 2026-01-04T04:10:52.120Z Authors: Ashish Vaswani, Prajit Ramachandran, Aravind Srinivas, Niki Parmar, Blake Hechtman, Jonathon Shlens Published: 2021-03-23T17:56:06.000Z ## Abstract Self-attention has the promise of improving computer vision systems due to parameter-independent scaling of receptive fields and content-dependent interactions, in contrast to parameter-dependent scaling and content-independent interactions of convolutions. Self-attention models have recently been shown to have encouraging improvements on accuracy-parameter trade-offs compared to baseline convolutional models such as ResNet-50. In this work, we aim to develop self-attention models that can outperform not just the canonical baseline models, but even the high-performing convolutional models. We propose two extensions to self-attention that, in conjunction with a more efficient implementation of self-attention, improve the speed, memory usage, and accuracy of these models. We leverage these improvements to develop a new self-attention model family, HaloNets, which reach state-of-the-art accuracies on the parameter-limited setting of the ImageNet classification benchmark. In preliminary transfer learning experiments, we find that HaloNet models outperform much larger models and have better inference performance. On harder tasks such as object detection and instance segmentation, our simple local self-attention and convolutional hybrids show improvements over very strong baselines. These results mark another step in demonstrating the efficacy of self-attention models on settings traditionally dominated by convolutional models. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on ImageNet 1k (val) (Accuracy & TPU Efficiency)](): Image Classification; ImageNet-1k (val); Top-1 Accuracy: 85.8. - [Image Classification on ImageNet-1K (test) (Standard Accuracy)](): Image Classification; ImageNet 1k (test); Top-1 Accuracy: 85.8. - [Image Classification on ImageNet-1K 1.0 (val)](): Image Classification; ImageNet-1K 1.0 (val); Top-1 Accuracy: 85.8. - [Image Classification on ImageNet 1k (test)](): Image Classification; ImageNet 1k (test); Top-1 Accuracy: not recorded. ## Original sources and implementation [Original paper]() [PDF]()