# All the attention you need: Global-local, spatial-channel attention for image retrieval

**Authors:** Chull Hwan Song, Hye Joo Han, Yannis Avrithis  
**arXiv:** [2107.08000](https://arxiv.org/abs/2107.08000)  
**Date:** 2021-07-16  
**Summary page:** https://www.summarizepaper.com/en/paper/2107.08000v1/

## Key points

- Authors emphasize the significance of spatial pooling and attention mechanisms in developing a robust global image representation.
- Integration of all four forms of attention (local, global, spatial, and channel) through the global-local attention module (GLAM) at the end of a backbone network is highlighted.
- The model effectively learns a powerful embedding for image retrieval with an emphasis on global descriptors.
- Empirical evidence showcases significant performance improvements compared to standard benchmarks by leveraging all forms of attention within GLAM.
- The exploration underscores the importance of considering multiple dimensions in designing effective representation learning models for large-scale image retrieval tasks.

## Summary

In their paper titled "All the attention you need: Global-local, spatial-channel attention for image retrieval," authors Chull Hwan Song, Hye Joo Han, and Yannis Avrithis delve into representation learning for large-scale instance-level image retrieval. They emphasize the significance of spatial pooling and attention mechanisms in developing a robust global image representation beyond just the backbone network, training pipelines, and loss functions. By integrating all four forms of attention - local and global, spatial and channel - at the end of a backbone network through their novel global-local attention module (GLAM), the authors demonstrate how this model can effectively learn a powerful embedding for image retrieval with an emphasis on global descriptors. Through empirical evidence showcasing the synergistic interaction of all forms of attention within GLAM, they showcase significant improvements in performance compared to standard benchmarks. This comprehensive exploration of various attention mechanisms underscores the importance of considering multiple dimensions in designing effective representation learning models for large-scale image retrieval tasks.