mlsanityData to 14 Oct 2026

Deep Attention Unet: A Network Model with Global Feature Perception Ability

arXiv 2304.10829 ↗First posted 21 Apr 2023cs.CV eess.IVcited by 0

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Abstract (arXiv)

Remote sensing image segmentation is a specific task of remote sensing image interpretation. A good remote sensing image segmentation algorithm can provide guidance for environmental protection, agricultural production, and urban construction. This paper proposes a new type of UNet image segmentation algorithm based on channel self attention mechanism and residual connection called . In my experiment, the new network model improved mIOU by 2.48% compared to traditional UNet on the FoodNet dataset. The image segmentation algorithm proposed in this article enhances the internal connections between different items in the image, thus achieving better segmentation results for remote sensing images with occlusion.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 15 references that are in the map. First posted on arXiv 21 Apr 2023.

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References · 12

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  5. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  6. 2020End-to-End Object Detection with Transformerscited by 7,414
  7. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,525
  8. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,435
  9. 2020FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understandingcited by 181
  10. 2018DUNet: A deformable network for retinal vessel segmentationcited by 90
  11. 2018MDU-Net: Multi-scale Densely Connected U-Net for biomedical image segmentationcited by 27
  12. 2018Dense Multi-path U-Net for Ischemic Stroke Lesion Segmentation in Multiple Image Modalitiescited by 13

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