English(EN) ACSAN: All-Convolutional Self-Attention Network Integrating Convolutional Operations with Vision Transformer

新型ACSAN模型集成卷积与视觉Transformer

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

研究人员推出全卷积自注意力网络（ACSAN），这是一种新颖的深度学习模型，旨在提高视觉Transformer（ViTs）的效率和适用性。ACSAN集成了卷积运算，以取代ViTs中常见的全连接矩阵乘法。该模型采用动态卷积自注意力（DCSA）模块，该模块使用多尺度空洞卷积来生成查询（Query）、键（Key）和值（Value），并结合了空间和通道自注意力机制。在CIFAR-10、CIFAR-100和ImageNet-1k数据集上的实验表明，与标准的ViTs和其他最先进的方法相比，ACSAN在参数更少的情况下实现了具有竞争力的分类准确率。 AI

影响为视觉Transformer在图像分类任务中提供了一种更具参数效率的替代方案。

排序理由该集群包含一篇详细介绍新型深度学习模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Zimian Wei, Tianqi Zhang, Hengyue Pan · 2026-10-27 04:00

ACSAN：将卷积运算与Vision Transformer相结合的全卷积自注意力网络

arXiv:2610.35375v1 Announce Type: cross Abstract: In the past few years, Vision Transformer (ViT) has achieved excellent performance in the field of computer vision. However, there still some problems remain. For instance, ViTs heavily rely on large-scale training data since they…

本文如何被排名

报道来源 [1]

ACSAN：将卷积运算与Vision Transformer相结合的全卷积自注意力网络

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