Spanet: Spatial Pyramid Attention Network for Enhanced Image Recognition

By Jingda Guo, Xu Ma, Andrew Sansom, M. McGuire, Andrew Kalaani, Qi Chen, Sihai Tang, Qing Yang, Song Fu. 2020

99 citations recorded by the source corpus.

Attention mechanism has shown great success in computer vision. In this paper, we introduce Spatial Pyramid Attention Network (SPANet) to investigate the role of attention block for image recognition. Our SPANet is conceptually simple but practically powerful. It enhances the base network by adding Spatial Pyramid Attention (SPA) Blocks laterally. In contrast to other attention based networks that leverage global average pooling, our proposed SPANet considers both structural regularization and structural information. Furthermore, we investigate the topology structure of attention path connection and present three SPANet structures. SPA block is flexible to be deployed to various convolutional neural network (CNN) architectures. The experimental results show that our SPANet significantly improves the recognition accuracy without introducing much computation overhead compared with other CNN models. Codes are made publicly available11https://github.com/13952522076/SPANet

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Neurology, Model Architectures

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Shiji Song
  2. Yulin Wang
  3. Gao Huang
  4. Le Yang
  5. Yizeng Han
  6. Honghui Wang
  7. Quoc V. Le
  8. Alan L. Yuille

In Computer Vision and Pattern Recognition

  1. Shiji Song
  2. Yulin Wang
  3. Gao Huang
  4. Yizeng Han
  5. Le Yang
  6. Honghui Wang
  7. Luc Van Gool
  8. Alan L. Yuille

In Neurology

  1. Gao Huang

In Model Architectures

  1. Shiji Song
  2. Yulin Wang
  3. Gao Huang
  4. Honghui Wang
  5. Le Yang
  6. Yizeng Han
  7. Quoc V. Le
  8. Alan L. Yuille

Citations in the corpus