UPANets: Learning from the Universal Pixel Attention Neworks

Ching Hsun Tseng, Shin Jye Lee*, Jianan Feng, Shengzhong Mao, Yu Ping Wu, Jia Yu Shang, Xiao Jun Zeng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


With the successful development in computer vision, building a deep convolutional neural network (CNNs) has been mainstream, considering the character of shared parameters in a convolutional layer. Stacking convolutional layers into a deep structure improves performance, but over-stacking also ramps up the needed resources for GPUs. Seeing another surge of Transformers in computer vision, the issue has aroused severely. A resource-hungry model is hardly implemented for limited hardware or single-customers-based GPU. Therefore, this work focuses on these concerns and proposes an efficient but robust backbone, which equips with channel and spatial direction attentions, so the attentions help to expand receptive fields in shallow convolutional layers and pass the information to every layer. An attention-boosted network based on already efficient CNNs, Universal Pixel Attention Networks (UPANets), is proposed. Through a series of experiments, UPANets fulfil the purposes of learning global information with less needed resources and outshine many existing SOTAs in CIFAR-{10, 100}.

Original languageEnglish
Article number1243
Issue number9
StatePublished - Sep 2022


  • attention
  • CNN
  • computer vision
  • image classification


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