LA-layer: General local attention layer for full attention networks

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Attention layers have contributed to state-of-the-art results on vision tasks. Still, they leave room for improvement because position information is used in a fixed manner, and the computation cost is typically high. To mitigate both issues, we propose a convolution-style local attention layer (LA-layer) as a replacement for traditional attention layers. LA-layers not only encode the position information of pixels in a convolutional manner, but also produce position offsets following a novel constrained rule so that keys will deform and result in larger receptive fields. Query and keys are processed by a novel aggregation function that outputs attention weights for the values. In our experiments with different types of ResNets, we replace convolutional layers with LA-layers and address image recognition, object detection and instance segmentation tasks. We consistently demonstrate performance gains, despite having fewer FLOPs and training parameters. Our code is available at:

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE International Conference on Multimedia and Expo, ICME 2023
Number of pages6
ISBN (Electronic)978-1-6654-6891-6
Publication statusPublished - 25 Aug 2023

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X


  • Local attention
  • CNN
  • Deformable Kernel
  • Convolutional neural network


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